KEF, Alm.del - 2022-23 (2. samling) - Bilag 228: Henvendelse af 12/4-23 fra Maia Bjørka Hansen, Svaneke, om WHO og Sundhedsstyrelsens anbefalinger vedr. nye højspændingsanlæg ifm. Energiø Bornholm

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International Journal of

Molecular Sciences

Review

Magnetic Fields and Cancer: Epidemiology, Cellular Biology,

and Theranostics

Massimo E. Maffei

Department Life Sciences and Systems Biology, University of Turin, Via Quarello 15/a, 10135 Turin, Italy;

[email protected]; Tel.: +39-011-670-5967

Citation:

Maffei, M.E. Magnetic

Fields and Cancer: Epidemiology,

Cellular Biology, and Theranostics.

Int. J. Mol. Sci.

2022,

23,

1339.

https://doi.org/10.3390/

ijms23031339

Academic Editor: Maurizio Battino

Received: 30 December 2021

Accepted: 22 January 2022

Published: 25 January 2022

Publisher’s Note:

MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional afﬁl-

iations.

Abstract:

Humans are exposed to a complex mix of man-made electric and magnetic ﬁelds (MFs)

at many different frequencies, at home and at work. Epidemiological studies indicate that there is

a positive relationship between residential/domestic and occupational exposure to extremely low

frequency electromagnetic ﬁelds and some types of cancer, although some other studies indicate

no relationship. In this review, after an introduction on the MF deﬁnition and a description of

natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure

to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo

and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with

particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer

diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic

resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic

nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise

delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of

cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and

methodologies in epidemiological and cell biology studies. Although scientists do not generally

agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be

the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which

could trigger or enhance the expression of oncogenes.

Keywords:

magnetic ﬁeld; cancer; epidemiology; therapy; diagnostics; theranostic; MRI; magnetic

nanoparticles; nanomedicine; reactive oxygen species

1. Introduction

Public concern about electromagnetic ﬁelds (EMFs) from power systems is increasing

along with the electricity demand, wireless technologies, and changes in work systems and

social behavior [1–4]. For modern populations, extremely low-frequency (ELF) electric and

magnetic ﬁelds (MFs) are common exposures and complex biological mechanisms underly

the potential effects of externally-applied MFs [5,6]. In 2002, the International Agency for

Research on Cancer (IARC) categorized ELF (including the power frequencies of 50 and

60 Hz) MFs as “possibly carcinogenic to humans” [7].

Controversial and often contradictory scientiﬁc reports continue to stimulate debates

on the biological effects of EMFs, often leading to confusion and distraction which hamper

the development of univocal conclusions on the real hazards that are caused by EMFs [8].

In this review the association between MF and cancer will be reviewed by considering

the effect of MF in causing cancer as well as the application of MF as a therapeutic and

diagnostic (theranostic) tool. Epidemiological studies, including both domestic/residential

and occupational data, as well as human and animal cell studies that were published in the

last 20 years will be also considered to provide an overview of the state of the art literature.

The strategy that was implemented to carry out this review was based on a deep

search in the databases Web of Science (2000–2021), PubMed (2000–2021), and the EMF

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

Int. J. Mol. Sci.

2022,

23,

1339. https://doi.org/10.3390/ijms23031339

https://www.mdpi.com/journal/ijms

KEF, Alm.del - 2022-23 (2. samling) - Bilag 228: Henvendelse af 12/4-23 fra Maia Bjørka Hansen, Svaneke, om WHO og Sundhedsstyrelsens anbefalinger vedr. nye højspændingsanlæg ifm. Energiø Bornholm

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Portal (https://www.EMF-portal.org/en, accessed on 1 December 2021). By considering as

entries the terms “cancer” AND “magnetic ﬁeld” the total number of Web of Science Core

Collection papers in the period from January 2000 to December 2021 was 12,364, whereas,

for the same period, the total number of papers in PubMed was 11,539. The selection of

papers was done on the terms: diagnostics, therapy, epidemiology, policy, along with a

selection of cancer types, and the exclusion criteria was the impossibility to obtain a full

text or the lack of speciﬁcity with the selected areas of the review.

Despite the narrative nature of this view, quantitative data on MF exposure and

methodologies are described in ﬁve supplementary Tables (Supplementary Tables S1–S5),

whereas a supplementary data set (Supplementary Data Set S1, EndNote ﬁle) contains all

references that were cited in this article in addition to many other references.

1.1. Deﬁnition and Natural/Anthropogenic Sources of Magnetic Fields

EMFs are present everywhere in our environment. Electric ﬁelds are produced by the

local build-up of electric charges in the atmosphere that are associated with thunderstorms.

The Earth’s MF, or geomagnetic ﬁeld (GMF), is the principal source of static ﬁelds (SFs) [9].

It interacts with the geosphere and the biosphere and plays a major role in shielding the

harmful effects of cosmic radiation. Different areas inside our planet are responsible for the

GMF which can be represented as the sum of MFs of several sources:

FT = F

+ F

δF

where F

is the dipolar component of the GMF, F

is the ﬁeld of world anomalies that

are associated with the heterogeneity of the planet interior (non-dipolar ﬁeld), F

is the

magnetization of rocks in the Earth’s crust (anomalous ﬁeld), F

is the external sources

ﬁeld, and

δF

is the ﬁeld variation that is also associated to external causes. The main GMF

is also represented by the sum of the dipolar and non-dipolar ﬁelds (F = F

+ F

The GMF is composed of three orthogonal vectors:

X, Y,

and

The combination of the

two horizontal vectors yields the horizontal component

which is aligned in the direction

of the compass needle and that can be expressed as:

Whereas the total ﬁeld intensity, which at the poles is directed towards the center of

the planet, can be expressed as:

The angle that is formed between

and the geographic north is the declination,

whereas the inclination,

is the angle between the horizontal plane and the vector of

total ﬁeld intensity

The international SI system the magnetic induction or magnetic ﬂux

density (B) is measured in Tesla (T) and its subunits (µT = 10

−

T; nT = 10

−

T). One tesla

equals one Weber per square meter, corresponding to 10

gauss (G), which is the unit of

magnetic ﬁeld in the centimeter-gram-second system. Thus, 1 G = 100

µT.

The magnetic ﬂux density,

is linked to the magnetic ﬁeld strength,

by a material

constant, the magnetic permeability

(also called magnetic conductivity).

The magnetic permeability,

µ,

is a measure of the permeability of materials for MFs.

The power ﬂux density,

of the EMF consists of energy fractions of the electric and

MF components and is measured in Watts per square meter (W m

−

). The ﬁeld strength

decreases with increasing distance from the ﬁeld source.

The strength of the GMF at the surface of the Earth ranges from over 60

µT

around the

magnetic poles in northern Canada, the south of Australia, and in parts of Siberia to less

Int. J. Mol. Sci.

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than 30

µT

in an area that includes most of South America and South Africa (the so-called

South Atlantic anomaly) [10]. In the Earth’s history, the GMF has changed with the so-

called geomagnetic reversals, where the GMF was characterized by periods (more-or-less

extended) with the same polarity. These reversals occurred some hundred times since the

Earth’s formation, with intervals between the polarity phases estimated around 300,000

years. The present normal polarity started around 780,000 years ago; therefore, an imminent

geomagnetic reversal would not be so unexpected. The South Atlantic anomaly, a zone

with signiﬁcant reduction of the GMF intensity that is located in front of Brazil/Argentina,

could be the initial symptom of a future change of polarity [2]. Changes in GMF intensity

imply a reduction of the GMF shield against cosmic radiation, with possible consequences

for all living organisms, which cannot avoid the effects of the GMF [11].

It is not clear whether the GMF can contribute to potential health risks, being present

in our planet before the evolution of living organisms; however, one question remains

whether different values of GMF in different countries in which epidemiological studies

of anthropogenic sources of MF have been performed might affect the results of those

studies [12].

Besides natural sources, the EM spectrum also includes ﬁelds that are generated by

human-made sources. For instance, X-rays are generated and used for diagnosis, power

sockets are associated with low frequency electromagnetic ﬁelds (LF EMFs), and various

kinds of higher frequency radio waves are used to transmit information—whether via TV

antennas, radio stations, or mobile phone base stations.

Static magnetic ﬁelds (SMF) (with direct current, DC) or alternating magnetic ﬁelds

(AMF) (with alternate current, AC) are formed, depending on the current feed. The polarity

of AMF changes according to the cyclic changes in the direction of the current ﬂow (e.g., 100

polarity changes per second with 50 Hz AC), whereas in SMFs, the polarity is unchanged.

The scientiﬁcally documented interaction with the organism allows the classiﬁcation of

non-ionizing electromagnetic ﬁelds (NI EMF) into low frequency (LF) and radio frequency

(RF). The stimulation or excitation of nerves, muscles, and sensory receptors may occur

below a threshold of 1 MHz; however, values that are higher than 1 MHz generate only

thermal effects. EMFs and radiation cover a wide frequency range. The NI radiation range

of the EM spectrum up to 300 GHz comprises SMF (0 Hz) and LF ﬁelds (

≤

300 Hz), the

intermediate-frequency range between approximately 300 Hz and 10 MHz, and the RF

range from 10 MHz to 300 GHz.

Table

summarizes the classiﬁcation of MFs based on type of radiation, ﬁeld, fre-

quency, and wavelength along with some examples and general effects. Static, non-ionizing

electric, and MFs that occur as a by-product. SF (0 Hz) occurs in batteries, at high voltage

direct current transmission lines (HVDC lines), where underground cables are present, with

permanent magnets, between objects with different electrical charges, and in the GMF. In

general, the kind of the MF and the level of the magnetic ﬂux density correspond to those

of the GMF. Inside of the converter stations, SMFs occur, and their strengths depend on the

voltage and the amount of ﬂowing current. In medicine, strong SMF are used in magnetic

resonance imaging (MRI, see also Section

4.1).

During an MRI procedure the patient is

exposed a strong SMF normally from 1.5–3 T. Only in research facilities, magnetic ﬁelds

from 7 T up to 9.4 T are used. A huge diversity of products with magnets that are in close

proximity to their surface in pillows, belts, bracelets, blankets, pendants, patches, or insoles

exhibit SMF in the range between 0.03 and 0.3 T. However, the levels are reduced to a tenth

at a 3–4 mm distance from the surface. Therefore, at a distance of several centimeters, the

magnetic ﬂux density lowers down to that of the natural GMF.

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Table 1.

Types of magnetic ﬁelds.

Type of

Radiation

Type of

Field

Frequency

Wavelength

Use

Examples

GMF, permanent magnets, transmission

lines, HVDC lines, batteries, between

objects with different electrical charges,

MRI

Technical appliances such as power lines,

wiring and household appliances such as

appliances for heating (e.g., electric cooker,

electric heating, washing machine, electric

water heater, iron), appliances with a

transformer or magnetic coils (e.g., radio

clock, low-voltage halogen lamps,

television set, WiFi) and appliances with an

electric motor (e.g., vacuum cleaner, drill,

hand blender, hair dryer, electric cars)

Effect

SMF

0 Hz

N.A.

Action of

force

AMF

0.3 Hz

3 Hz

16 2/3 Hz

50 Hz

300 Hz

3 kHz

30 kHz

100,000 km

18,000 km

6000 km

1000 km

100 km

10 km

Low

Frequency

traction

current and

three phase

alternating

current

Stimulation/

irritation

AMF

300 kHz

3 MHz

30 MHz

300 MHz

3 GHz

30 GHz

300 GHz

3 THz

1 km

100 m

10 m

10 mm

1 mm

100

µm

Induction cookers and electronic article

surveillance systems in stores, as well as

many industrial and medical applications,

PC monitors, mobile phone, microwave

ovens, radar stations. Broadcasting

Radio

frequency.

frequencies (short wave, AM, and FM

radio), digital television (digital video

Ra-

dio/television, broadcasting-terrestrial, DVBT) and digital

mi-

radio (digital audio broadcasting, DAB).

crowaves,

Wireless local area networks (WiFi, WLAN),

terahertz

cordless telephones, Bluetooth devices,

baby monitors, electronic article

waves

surveillance systems and RFID (radio

frequency range), radar systems, radio relay

systems, satellite TV and satellite Internet,

radio solutions for stationary Internet

Infrared

Visible

Light

Bulb lamps, heaters, body scanners for

security control

Thermal

effect

AMF

30 THz

300 THz

380 THz

789 THz

µm

780 nm

380 nm

100 nm

10 nm

1 nm

100 pm

10 pm

1 pm

Thermal

effect

Ionizing

AMF

UV-light,

X-rays,

gamma rays

Nuclear power plants, X-ray machines,

radioactive material.

Ionization

LF refers to the frequency range 0–100 kHz. The energy of the EMF that is absorbed in

biological tissue and is converted into heat deﬁnes the speciﬁc absorption rate (SAR) that

is obtained by exposure to a frequency that is between 100 kHz and 10 GHz. The SAR is

expressed in Watts per kilogram of tissue (W kg

−

) based on an average exposure time

of six minute intervals, during which a balance between the energy input and the heat

dissipation in the tissue is reached. It is possible to distinguish between the exposure of the

whole body or parts of the body by averaging over different body masses. All electrical

applications that are run on power supply (railways, electrical appliances in the home, and

at working places) lie in the range of LF AMF up to 1 kHz (wavelengths larger than 300

km) (low frequency (0.1 Hz–1 kHz)). No extremely LF AF occur in nature. ELF AFs are

generated by technical appliances such as power lines, wiring, and household appliances.

ELF Electric and MFs are generated by the power lines and their strength and distribution

Int. J. Mol. Sci.

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in the area surrounding the power lines depend on several parameters (including voltage,

amperage, tower shape, as well as alignment, and number, and slackness of the lines).

The strength of electric ﬁeld is mainly found beneath the power lines; however, this effect

rapidly diminishes with increasing distance from the power line [13]. Electric cars are a

signiﬁcant source of very high MFs due to the electric motor and large batteries, especially

during starting and stopping. In electric cars ELF MF dominate, but intermediate frequency

ﬁelds can occur also [14].

In the natural environment EMFs with intermediate frequencies (1 kHz–10 MHz) can

be generated during the so-called sferics, which are broadband EM impulses that occur in

the Earth’s atmosphere as a consequences of lightning discharges. Sferics may extend from a

few kHz to several tens of kHz (3–100 kHz) [15]. Intermediate frequency includes the lower

range of the radiofrequency band with its corresponding applications, but also applications

that are working with speciﬁc frequencies, such as induction cookers and electronic article

surveillance systems in stores, as well as many industrial and medical applications.

Radio frequency (30 kHz–300 GHz) includes a range of "broadcasting frequencies"

(between 30 kHz and 300 MHz; wavelengths from 10 km to 1 m) covering long wave

radio broadcasting, amplitude modulation (AM) radio broadcasting, shortwave radio

broadcasting, and frequency modulation (FM) radio broadcasting (authorized in the very

high frequency range). Terahertz waves are also in the non-ionizing radiation spectral

range, between 300 GHz and 10 THz (wavelengths from 1 mm to 30

µm).

For example,

they are used for quality control of industrial products, at some airports in body scanners

for security control, or in skin cancer scanning systems [16].

The following range of 384 THz to 789 THz (780 nm to 380 nm) is referred to as visible

light. This is succeeded by the ranges of ultraviolet radiation and the ionizing radiation

with even shorter wavelengths.

As noted above, the magnetic ﬁeld strength around a conductor increases with rising

electric current strength and decreases with growing distance from the ﬁeld source. It is

dependent on the type of source how fast the ﬁeld decreases (Figure

1).

1.2. Public Health Initiatives and Concern

In 1996 the World Health Organization (WHO) launched a large, multidisciplinary

research effort to respond to growing public health concerns about the possible health

effects from exposure to EMF sources. The International EMF Project, open to any WHO

Member State government, brings together current knowledge and available resources of

key international and national agencies and scientiﬁc institutions. Among the aims and

scopes of the EMF Project are: (a) develop and publish a health risk assessment on EM RF

ﬁelds; (b) develop and disseminate information materials on risk management policies of

EMF; (c) provide technical support to national authorities and international organizations

regarding NI radiation; (d) establish an inter-agency committee on NI radiation safety to

exchange information and harmonize activities; and (e) develop international standards for

protection against NI radiation [17].

Worldwide, many countries set their own national standards for exposure based on

the guidelines that are set by the International Commission on Non-Ionizing Radiation

Protection (ICNIRP), a non-governmental organization that was formally recognized by

WHO. Risk assessment analyses that are based on publicly available data are used to help

formulate government guidance on occupational MF by also considering the cancer cases

that were prevented and the monetary beneﬁts accruing to society by reducing workplace

exposures [18]. An overview of the current knowledge regarding EMF-related health risks

including recommendations for the diagnosis, treatment, and accessibility measures of

electromagnetic hypersensitivity (EHS) to improve and restore individual health outcomes

as well as for the development of strategies for prevention has been recently published [19].

The International Radiation Protection Association (IRPA) represents national radiation

protection societies [20]. An updated and reliable source of information is provided by the

EMF Portal (https://www.EMF-portal.org/en, accessed on 30 December 2021).

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curity control, or in skin cancer scanning systems [16].

The following range of 384 THz to 789 THz (780 nm to 380 nm) is referred to as visible

light. This is succeeded by the ranges of ultraviolet radiation and the ionizing radiation

with even shorter wavelengths.

As noted above, the magnetic field strength around a conductor increases with rising

6 of 55

electric current strength and decreases with growing distance from the field source. It is

dependent on the type of source how fast the field decreases (Figure 1).

Figure 1. The magnetic field intensity decreases with growing distance from the field source.

Figure 1.

The magnetic ﬁeld intensity decreases with growing distance from the ﬁeld source.

In general, the type and extent of the cautionary policy that is chosen critically depends

on the strength of the evidence for a health risk and the scale and nature of the potential

consequences. In many countries, the adoption of a principle of caution or prudent avoid-

ance implies the low-cost avoidance of unnecessary exposure as long as there is scientiﬁc

uncertainty about its health effects [21,22]. However, still some policies are effective in

preventing new situations with long-term exposure of children to MFs from overhead

power lines, but these generally do not include underground cables and other sources of

MFs [23]. Preventive measures and precautionary principles are necessary to warrant the

reduction of exposure to children because of their greater sensitivity to ELF EMF [24–26].

The American Academy of Pediatrics set out new recommendations to decrease the adverse

effects of exposure on children also to mobile phones [27].

2. Epidemiological Studies Evaluating MF and Cancer Relationships

It is known that epidemiological studies alone cannot be used to determine a clear

cause and effect relationship when considering MF and cancer. This is mainly because

epidemiological studies evaluate only the statistical associations between exposure and

disease, which may not be necessarily caused by the exposure. Only the presence of a

consistent and strong association between the exposure and the effect, a clear dose-response

relationship, support that is provided by relevant animal studies, a credible biological

explanation, and above all, if there is consistency between the studies can support cause

and effect conclusions. In studies involving EMF and cancer most of these factors are

generally missing. Studies of the potential health effects of EMF have concentrated on the

MF because it is generally assumed to be the component that is most likely to have biological

effects [28]. Since the ﬁrst evidence determining the relationship between the ELF EMF and

leukemia in children [29], epidemiological studies in this context increased and the IARC

classiﬁed the ELF EMF in group 2B, a “possible carcinogen” to humans, whereas static

electric and MFs are not classiﬁable as carcinogenic to humans (Group 3) [30]. Although

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it is generally accepted that EMFs can exert biological effects, in general, epidemiological

studies show a weak and sometimes inconsistent association between exposure to power-

frequency ﬁelds (PFF) and cancer. In most cases, the studies fail to show a dose-response

relationship [31,32]. The opposite happens in laboratory studies where PFF points towards

causing or contributing to cancer (see below). The application of “Hill’s criteria” (i.e.,

strength, plausibility, speciﬁcity, biologic gradient, consistency, coherence, experimental

evidence, temporality, and analogy) to laboratory and epidemiological studies shows a

weak evidence for a causal association between cancer and the exposure to PFF [33].

Because cancer is one of the signiﬁcant problems of global health, epidemiologic

studies have faced the question of whether occupational and residential exposure to ELF

EMF might be carcinogenic. There are three main explanatory hypotheses that appear in

the literature: (i) the EM hypothesis, attributing EHS to EMF exposure; (ii) the cognitive

hypothesis, assuming that EHS results from false beliefs in EMF harmfulness; (iii) the

attributive hypothesis, considering EHS as a surviving strategy for pre-existing condi-

tions [34]. Most of the epidemiologic studies explored the association between ELF EMFs

and the susceptibility to different cancers. In the next section the residential/domestic and

the occupational exposure to MF as related to cancer occurrence will be described.

2.1. Epidemiology of Residential/Domestic Exposure to MF

A survey of the literature indicates that residential exposure to EMFs is associated to an

increased risk of cancers, particularly breast cancer, brain tumors, and leukemias. However,

most of these studies are based on small numbers of high ﬁeld-exposed cases [16,35,36]

and an increasing number of studies does not support an epidemiologic association of

adult cancers with residential MFs [35–37]. Many of the studies clearly have shortcomings,

which often prevent any ﬁrm conclusions [38,39]. Moreover, the indirect measures of EMF

exposure used may also correlate with other factors such as social status (e.g., age, race,

gender) or environmental pollution. It is possible that these unconsidered and confounding

factors may contribute to the cancer rates that are reported and also to the contrasting

results that are reported in various EMF studies [40]. Indeed, EMF, which itself is not

believed to be genotoxic, could inﬂuence carcinogenesis if it exerted either direct or indirect

effects on target cells [41].

Another important issue is the exposure assessment. The exposure to MF can vary

greatly over time and distance, has multiple sources, and is imperceptible and ubiqui-

tous [42–45]. In exposed schools, children may experience a higher chance of receiving a

mean exposure >0.4

µT

during school hours [46,47]; whereas those living in big buildings or

using electric heating appliances in larger families had a generally higher level of personal

indoor exposure [48]. Based on the known location of domestic and service MF sources,

apartments can be reliably classiﬁed as high and low MF-exposed [49–51]. Methodologies

for estimating MF at study residences as well as for characterizing the sources of uncer-

tainty in these estimates have been developed [52]. In residential/domestic epidemiological

studies, geographic information that is collected in an exact place of residence at the time

of cancer diagnosis can provide several strategic geophysical elements for assessment [53].

The estimation of the overall exposure level from a single address is also informative [54]. In

general, the public health impact of residential ﬁelds is considered limited, but the available

data show the occurrence of both no impact and substantial impact possibilities [55].

2.1.1. Brain Tumor

The incidence of primary brain tumors has increased in many countries world-

wide [56,57] and gliomas are the most frequent primary brain tumors in adults [58]. Res-

idential exposure during childhood to EMF produced inconsistent results and a lack of

an association when related to brain cancers, regardless of the exposure metrics that were

used whether based on wire codes, distance, or the measured or calculated ﬁelds [59].

When focusing on the health effects, the most studied sources of ELF MF are power lines.

Exposure to ELF MF that is emitted by power lines can be assessed by direct methods that

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rely on measurements at a given place over a time range [60] or by individual monitoring

through measuring ELF MF exposures throughout the day by wearable dosimeters [61].

Both methods give little or no information on historical exposure to ELF MF. Indirect

methods include geographical information system (GIS) which have been used along with

declarative data, such as residential history, to assess residential ELF MF exposure in the

general population [62–65]. Case-control studies that are based on death certiﬁcates re-

vealed an association between adult brain tumor mortality and living less than 50 m (odd

ratios, OR 1.10 95%, CI 0.74–1.64) [66] or 100 m (OR 2.99, 95%, CI 0.86–10.40) [67] from

power lines. In a recent work, signiﬁcant associations were found between the cumulated

duration living at <50 m to high voltage lines (50/60 Hz, 0.3

µT)

and all brain tumors (OR

2.94; 95%CI 1.28–6.75) and glioma (OR 4.96; 95%CI 1.56–15.77) [65], conﬁrming previous

studies [35,66–69]. Contrasting results have been reported for brain tumors in children. In

childhood brain cancer, with the exception of the possibility of a moderate risk increase in

high cut-point analyses (0.3/0.4

µT),

no increased risk was evident for different exposure

metrics [70,71], whereas children whose MF exposure level was above 0.3 or 0.4

µT,

elevated risk of brain tumor was observed [72–74]. In residential areas, the transient electric

and MFs would induce higher current density in the child‘s body than power frequency

ﬁelds with similar ﬁeld strength [75]. In some studies, there is no evidence for a role of ELF

cellphone EMFs in childhood brain cancer [27].

2.1.2. Breast Cancer

Breast cancer threatens women with the highest incidence and second highest mortality

rate of all cancers and in women aged 65 and older when nearly one half of all new breast

cancer is diagnosed [76]. The excessive exposure to MFs increases the risk of female breast

cancer, as demonstrated in several pooled or meta-analyses as well as subsequent peer-

reviewed studies [69]. It is questionable whether chronic human exposure to MFs might

affect melatonin secretion, its circadian rhythm, or both [77–80]. In general, no cumulative

effects on melatonin secretion in humans have been found in response to MFs and this

rebuts the “melatonin hypothesis” in which a decrease in plasma melatonin concentration

(or a disruption in its secretion) would be correlated with the occurrence of breast cancers

as a consequence of exposure to MFs [81–88]. Indeed, MF exposure correlates with an

increased proliferative activity of the mammary epithelium, which is a likely explanation

for the cocarcinogenic or tumor-promoting effects of MF exposure that is observed [89].

However, some authors found a motivation for going back to the melatonin hypothesis

in relation to data, suggesting magnetosensory disruption by ELF MF in mammals, and

magnetosensitivity in humans, along with the inﬂuence of MFs on circadian rhythmicity

with a consequent disruption of non-photic sensory stimuli of various nature [90].

In studies that were based on the measurement of an electric bedding device, only

premenopausal breast cancer (OR = 1.23; 95% Cl: 1.01, 1.49,

= 0.04) showed a slight

increased risk [91]. Breast cancer was found to increase with the number of years and

seasons of bedding device use during sleep. Similar trends in dose response were shown in

both premenopausal and postmenopausal women and for both estrogen receptor-positive

and estrogen receptor-negative tumors. Therefore, there is a growing body of evidence

that the use of electric bedding devices may increase breast cancer risk [92]. In terms of

geographic variation of breast cancer rates, the results are inconclusive and do not support

a major role of MF risk factors in the etiology of breast cancer [93].

2.1.3. Leukemias

Leukemia is the most common cancer in children [94]. The analysis of reports on

childhood leukemia as related to exposure to MFs shows that a statistically signiﬁcant asso-

ciation between MF exposure and childhood leukemia is found in almost all government

or independent studies with an elevated risk of at least OR = 1.5, while a not signiﬁcant or

even suggestive association is reported in many industry supported studies [69].

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Several meta-analyses showed a statistical association between childhood leukemia

and a range of exposure 0.1–2.36

µT

MF intensity [74,95–99]. Children that are exposed

to elevated ELF-MF show relative risks of leukemia between 1.3 and 2 [100–102] and the

highest exposure was associated with an increased risk of B-lineage acute lymphoblastic

leukemia (B-ALL) when compared with lower exposures [103]. A signiﬁcant association

was observed during the night (OR = 3.21, 95% CI 1.33–7.80) between childhood leukemia

and MF exposure [104].

Several epidemiologic case-control studies examined the association between child-

hood cancer risk and distance to high-voltage overhead transmission lines (HVOTL).

Statewide, record-based case-control studies of childhood leukemia evidenced the oc-

currence of risk that was associated with greater exposure to MF that was generated in

areas that were close to power lines [66,105–108]. Living in polluted regions and pre-

and post-natal exposure to high voltage power lines has been described as risk factors

of acute lymphoblastic leukemia (ALL) in people of low socioeconomic status Iranian

population [109]. In children with ALL, ELF MF-exposure was found to have no impact on

the survival probability or risk of relapse [110].

The occurrence of childhood acute leukemia (AL) has been studied around nuclear

power plants (NPP). The results suggest a possible excess risk of AL in the close vicinity to

NPP [111]. The small, but statistically signiﬁcant increased incidence of AL in the surround-

ing of some NPP have motivated governments to work toward a better understanding of

the main causes of AL long-term strategic research agendas through interdisciplinary and

international efforts [112].

MFs are not the only factor that varies in the vicinity of MF sources, complicating

interpretation of any associations. Several reports demonstrate that MFs that are generated

by different sources are not an important cause of leukemia both in adults [37,113,114] and

children in many geographical areas [12,113–120]. Moreover, exposure levels in some big

cities are always signiﬁcantly far lower than 0.3–0.4

µT

[121].

The associations that were observed between power lines exposure and childhood

leukemia appears to be not related to mobility [122,123]. No indications were also found of

an association of risk for people that are exposed to magnetic ﬁelds from underground [124]

and above ground lines [106,107] or of a trend in risk with increasing MF for leukemia.

Residential proximity to transformer stations has been associated with a borderline risk of

childhood cancer [125].

Distance from HVOTLs during the year of birth is unlikely to be associated with an

increased risk of leukemia [73] and little evidence was found between exposure to MF

inside infant incubators and increased risk of childhood leukemia [126]. No statistically

signiﬁcant association was observed between wire codes and childhood leukemia [127].

Recently, a synoptic analysis provided evidence that the risk of childhood leukemia is not

increased by exposure to ELF EMFs, suggesting that IARC’s classiﬁcation of ELF EMF

needs revision [128].

By considering both signiﬁcant and not signiﬁcant correlations between MF and

leukemia, no major environmental risk factors (including MFs) have been established as

major contributors to the global leukemia burden, although distinct incidence patterns by

geography, age, and sex suggest a role of the environment in its etiology [129]. The results

may be affected by several sources of bias: analyses that are based on continuous exposure

show no exposure-disease association, while incoherent exposure-outcome relationships

characterize analyses that are based on categorical variables [130].

2.1.4. Other Cancers

EMF in the home-environment (color tv, computer monitor, microwave-oven, cellular

phone, etc.) might act as potential contributing factors for the development of cancer,

as well as exert indirect effects on humans. Microwave exposure induces L-amino acids

change to D-amino acids, and exposure of the human body to microwaves over a long

period of time may contribute to induction of cancer [131]. Exposure to EMF that is

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generated by electric blankets has been suggested to increase the risk of hormone-dependent

cancers such as testicular [132] and prostatic [133], but was not signiﬁcantly associated with

endometrial cancer risk [134]. MFs of industrial frequency have been shown to be a risk

factor for occurrence of oncological diseases in the population and an increased incidence of

malignant tumors has been noted as the induction of the magnetic ﬁeld that is produced by

HVOTL increases [135]. The incidence of melanoma has been linked to the distance to FM

broadcasting towers. A correlation between melanoma incidence and the number of locally

receivable FM transmitters was found when geographic differences in melanoma incidences

were compared with the magnitude of this environmental stress. Therefore, melanoma

might be associated with exposure to FM broadcasting [136]. By considering pregnancy

duration, neonatal birth weight, length, head circumference, gender, and con-genital

malformations, no signiﬁcant effects of ELF EMFs were observed; however, precautionary

measures are necessary to reduce exposure to EMFs by pregnant women [137].

The Supplementary Table S1 reports the relationship between MFs and cancer in

epidemiologic studies that are related to domestic/residential exposure to MFs. The type

of cancer, study design, source of MF, range of MF exposure, location of the epidemiologic

study, and the main conclusions are reported. Figure

summarizes the epidemiology of

residential/domestic exposure to MF.

2.2. Epidemiology of Occupational Exposure to MF

During the last few decades, the intensity level of the EM occupational environment

has dramatically increased. By job category, the most highly exposed jobs (>0.23

µT)

included electronics workers, cooks and kitchen workers, cashiers, bakery workers, textile

machine operators, and residential and industrial sewing machine operators [138]. The

main components of EM pollution are in the ELF (10–300 Hz) and in ultra-low (ULF:

0–10 Hz) frequency bands [139]. Occupational epidemiology reveals that exposure to ELF

EMF is generally greater than that in the general population and concerns a large number of

workers in a variety of industries (see [140,141] for a historical overview of the occupational

EMF epidemiology).

MF exposure limits are more than a thousand times higher than the magnitudes that are

associated with the cancer risks that are observed in epidemiologic studies, leaving millions

of workers exposed to MF in this large gray area where the public health consequences

are unclear [140,141]. The International Labor Organization deﬁnes occupational exposure

as “exposure of a worker received or committed during a period of work” [142] while the

ICNIRP deﬁnes occupational exposure as “all exposure to EMF experienced by individuals

as a result of performing their regular or assigned job activities” [143].

As discussed for residential/domestic exposure to EMF, in the case of occupational

exposure, contrasting results have also been presented in relation to the co-occurrence of

different cancers including brain and breast cancer and hematological malignancies.

2.2.1. Brain Cancer

Occupational exposure to ELF EMF is a suspected risk factor for brain tumors; however,

the literature reports contrasting results. In adults, some meta-analyses of occupational

studies indicate a slightly higher risk for electrical workers, suggesting a small increase in

brain cancer risk [61,144,145], including childhood brain tumors [146], while others found

no evidence to support the hypothesis that exposure to MFs is a risk factor for gliomas or

meningiomas [147–150].

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mations, no significant effects of ELF EMFs were observed; however, precautionary measures

are necessary to reduce exposure to EMFs by pregnant women [137].

The Supplementary Table S1 reports the relationship between MFs and cancer in ep‐

idemiologic studies that are related to domestic/residential exposure to MFs. The type of

cancer, study design, source of MF, range of MF exposure, location of the epidemiologic

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study, and the main conclusions are reported. Figure 2 summarizes the epidemiology of

residential/domestic exposure to MF.

Figure 2. Summary of epidemiology of residential/domestic exposure to MF. The major confound‐

Figure 2.

Summary of epidemiology of residential/domestic exposure to MF. The major confounding

ing factors in epidemiological studies are shown along with the main geographical information that

factors in epidemiological studies are shown along with the main geographical information that is

is based on GIS (data management, hardware/software, topography, remote detection, and popula‐

based on GIS (data management, hardware/software, topography, remote detection, and population

tion demographics). Exposure assessment needs to be evaluated both outside and inside the resi‐

dence. The three major cancers are

needs to be evaluated both outside and inside the residence. The

demographics). Exposure assessment

represented: leukemia affects mainly childhood; brain cancer

increases with decreasing distances from EMF sources; and breast cancer is associated with mam‐

three major cancers are represented: leukemia affects mainly childhood; brain cancer increases with

mary epithelium proliferation and with exposure to bedding devices.

decreasing distances from EMF sources; and breast cancer is associated with mammary epithelium

proliferation and with exposure to bedding devices.

2.2.2. Breast Cancer

Several studies have evaluated the evidence linking women’s occupation and work-

place exposures to breast cancer. Overall, the data do not suggest that occupational expo-

sures to EMF increases the risk of breast cancer [144,145,151,152] with some exceptions [153].

Some studies have found an effect when looking at overall risk elevations in the women

studied [154] with increased risk among postmenopausal women but not premenopausal

women [155]. The hypothesis that daytime occupational exposure to MF enhances the

effects of nighttime light exposure on melatonin production (see above the “melatonin

hypothesis”) has been provided [153]. Occupational MF exposure has also been suggested

as a risk factor for breast cancer in men. An elevated risk of breast cancer was found in

men who were exposed to 0.6 T when compared to those with exposures <0.3 T; however,

large case-control studies of breast cancer in men that have been conducted to date provide

limited support for the hypothesis that exposure to MF increases the risk breast cancer in

men [156].

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2.2.3. Leukemias

Epidemiological studies addressing occupational ELF MF exposure and risk of leukemia

in adults have yielded slightly increased risks in exposed workers. Some studies show a

positive correlation between occupational MF and leukemias [157–160] and have suggested

that stronger effects may be observed for acute myeloid leukemia (AML) [161,162], chronic

myeloid leukemia (CML), ALL [138], lymphatic leukemia (LL) [163], and for chronic lym-

phocytic leukemia (CLL) [162]. In general, however, no clear exposure-response pattern has

emerged from the studies that evaluated exposure levels and some results do not support

an association between occupational ELF MF or electric shock exposure and AML [164].

Differences between the study designs or the populations that were studied might

be the cause of lack of consistency regarding the type of leukemia that is associated with

MF exposure, and still no ﬁrm conclusions can generally be drawn based on the existing

evidence [165–167]. No association was found between childhood leukemia risk and

parental occupational exposure to ELF EMF [168–170].

2.2.4. Other Cancers

In men, exposure to EMF showed an increased incidence of tumors of the liver, biliary

passages, kidney, and pituitary gland; for these cancer sites an exposure-response relation

was indicated [171]. There was very limited evidence for associations between occupational

ionizing radiation and testicular cancer, while there were some positive associations for

EMF [172].

Women that were exposed to MF showed an increased incidence of astrocytoma I-

IV, for cancer of the corpus uteri and multiple myeloma, with a clear exposure-response

pattern [171].

For both men and women, there was weak support for the hypothesis that occupa-

tional MFs exposure increased the risk of non-Hodgkin lymphoma [173], acoustic neu-

roma [174,175], and thyroid cancer [176], while sources that produce ELF ﬁelds were not

associated with neuroblastoma in offspring [177].

MF has previously been associated with mortality from acute myocardial infarction

(AMI) and arrhythmia but not from chronic coronary heart disease (CCHD) or atheroscle-

rosis in electric utility workers. For cumulative exposure, no association was observed

with mortality from AMI or CCHD, indicating no exposure-related risk increase for AMI

mortality, which does not conﬁrm previous results [178].

Supplementary Table S2 reports the relationship between MFs and cancer in epidemio-

logic studies that are related to the occupational exposure to MFs. The type of cancer, study

design, source of MF and occupation, range of MF exposure, location of the epidemiological

study, and the main conclusions are reported. Figure

summarizes the epidemiology of

occupational exposure to MF.

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Figure 3. Summary of epidemiology of occupational exposure to MF. There is still little evidence on

Figure 3.

Summary of epidemiology of occupational exposure to MF. There is still little evidence on

the relationship between occupational exposure to EMF and brain cancer, whereas several leuke‐

the relationship between occupational exposure to EMF and brain cancer, whereas several leukemias

mias have been associated with continuous exposure to ELF‐EMFs. Breast cancer occurs both in

have been associated with continuous exposure to ELF-EMFs. Breast cancer occurs

cancers

women

women and men and the risk increases in men that are exposed to 0.6 T. Other

both in

that are

associated with MF exposure include myeloma in women and several other types of cancer in both

and men and the risk increases in men that are exposed to 0.6 T. Other cancers that are associated

women and men.

with MF exposure include myeloma in women and several other types of cancer in both women

and men.

3. In Vivo and In Vitro Effects of Magnetic Fields on Cancer

In many studies ELF exposure causes significant changes in cell survival, cell cycle

In many studies ELF exposure causes signiﬁcant changes in cell survival, cell cycle

progression, DNA integrity, and proliferation [40,179]. The cellular response to ELF MF

progression, DNA

many parameters including osmolarity, frequency, waveform,

may depend on

integrity, and proliferation [40,179]. The cellular response to ELF

the

may depend on many parameters including osmolarity, frequency, waveform, the strength

strength and the exposure duration of the electromagnetic field, genetic/biological char‐

and the exposure duration of the electromagnetic ﬁeld, genetic/biological characteristics of

acteristics of the cells [180], specific metabolic state, or the specific stage in the cell cycle

the cells [180], speciﬁc metabolic state, or the speciﬁc stage in the cell cycle [180,181]. On

[180,181]. On the other hand, a common effect of exposure to SMF is the promotion of

the other hand, a common effect of exposure to SMF is the promotion of apoptosis and

apoptosis and mitosis, but not of necrosis or modifications of the cell shape [182,183]. The

mitosis, but not of necrosis or modiﬁcations of the cell shape [182,183]. The unbalance of the

unbalance of the apoptotic process could be linked to Ca

fluxes that are, in turn, depend‐

apoptotic process could be linked to Ca

ﬂuxes that are, in turn, dependent on the effect

ent on the effect on the plasma membrane that is exerted by SMF [184–187]. Other possible

on the plasma membrane that is exerted by SMF [184–187]. Other possible effects of SMFs

effects of SMFs that may lead to perturbation of the apoptotic rate, such as an alteration

that may lead to perturbation of the apoptotic rate, such as an alteration of the gene pattern

of the gene pattern expression or the increase of oxygen free‐radicals, could be, in turn, a

expression or the increase of oxygen free-radicals, could be, in turn, a co-carcinogenic

co‐carcinogenic factor leading normal cells, most likely with other sub‐lethal changes, to

contribute to the development of diseases [183].

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factor leading normal cells, most likely with other sub-lethal changes, to contribute to the

development of diseases [183].

The reduction of cell proliferation due to MF has been attributed to the interference of

signal transduction processes due to the tangential currents that are induced around the

cells [188]. The poor reproducibility might be caused by the period-dependent converse

cell growth due to the MF and might explain the adverse effects that are observed in several

experimental investigations [189].

Quantitative analyses of protein kinases C (PKC) expression patterns demonstrated

the translocation of PKC from the cytosolic to the membrane fraction was not affected by

MFs [190]. The phosphorylation of extracellular signal-regulated kinases 1/2 (ERK1/2)

is increased in response to ELF MF; however, the small increase in ERK1/2 phosphory-

lation is probably insufﬁcient to affect proliferation and oncogenic transformation [191].

Furthermore, repeated ELF EMF exposures did not show consistent response proﬁles

to time courses of immediate early genes, apoptotic genes, cell proliferation, and stress

response [192].

Small changes in transcription may occur in response to MFs [193,194]. Exposure to

900 MHz identiﬁed a differential expression of functional pathways genes [195]. Indeed,

epigenetic changes, including modiﬁcations of histones and microRNA expression and

DNA methylation, can be associated to ELF MF exposure [196–198]. However, in HeLa

cells, RNA polymerase-catalyzed RNA synthesis as well as DNA polymerase-catalyzed

DNA synthesis were found to not be statistically signiﬁcantly affected by 60 Hz 0.25–0.5 T

exposure for 0–60 min [199].

Aberrantly-expressed serum exosomal miRNAs upon MF exposure suggests a series

of informative markers to identify the exact dose of MF exposure [200]. ELF MF exposure

stabilizes active chromatin, particularly during the transition from a repressive to an active

state during cell differentiation [201]. Membrane receptors could be one of the most

important targets where ELF MF interacts with the cells [202] and RAS proteins, a member

of a large family of GTP-binding proteins that are involved in intracellular signal pathways,

may participate in the signal transduction process of 50 Hz MF [203]. EMF was also found

to affect the proteasome functionality, inducing an increase in its proteolytic activity [204].

To answer how MF may cause cancer, the action of MF as mutagenic agents and MF

involvement on chemical reactions that generate free-radicals have been considered. MF

does not seem to exert mutagenic effects and the generation of free-radicals that might be

linked to several other factors, beside the variability of MF exposure [40]. In the next part

of this section, the current data that are related to studies on human in vitro and cell-free

systems as well as in animal models will be discussed. This section will also discuss the

role of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and the role of

radical pair (RP) formation in MF-cancer relationship.

3.1. Studies on Human Cells (In Vitro Cellular Studies and Cell-Free Systems)

Exposure to ELF MFs combined with ionizing radiation do not suggest any synergistic

or antagonistic effects on human blood cells [205], whereas, by using sister chromatid

exchanges and comet assays, a slight but signiﬁcant decrease of cell proliferation was

evident in blood cells that were exposed to 980–1020

µT

[206].

Dermal ﬁbroblasts that were exposed to 1 mT from one to ﬁve hours showed a

reduction of their viability [207], while the rePETitive exposure to MF with ELF induced

DNA double-strand breaks and apoptosis in lung ﬁbroblasts [208]. However, a longer

exposure (48–72 h) to a reduced MF intensity (20–500

µT)

did not result in any appreciable

effect in the structural morphology and proliferation of human ﬁbroblasts [209].

Exposure of glioma cells to MF of 1.2

µT

intensity for three hours revealed changes

in both gene and protein expression. Microarray results showed an up-regulation of ﬁve

genes, whereas 25 genes were down-regulated upon MF exposure, suggesting a response

in the glioma cells to the MF treatment [210]. Proteomic studies on glioma SF767 cells show

a cytoskeletal intermediate ﬁlament protein increased following a low-level MF [211]. On

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the other hand no mechanisms that would explain the reported association between MF

and carcinogenesis were observed in H4 glioma cells [212].

In human keratinocytes, ELF EMF induces a slight oxidative stress that does not

overwhelm the metabolic capacity of the cells or have a cytotoxic effect when the cells are

exposed to 25–200

µT

MF intensity [213] but it does not affect melanin synthesis or skin

pigmentation [214].

The adherence of leucocytes and T-lymphocytes that were taken from cancer patients

strongly increased following exposure to sinusoidal MF at 50 Hz and 0.5–10 mT MF intensity

for one hour [215,216], whereas a low-frequency pulsing electromagnetic ﬁeld of 45 mT for

three hours induces cell death in native peripheral blood mononuclear proliferating cells

that were isolated from AML patients [217].

ELF MF increases the rate of cell death in normal human lymphoblastoid cell lines

and is ineffective on genetic instability syndromes cell lines, i.e., Fanconi anemia group C

and ataxia telangectasia, suggesting that the response of cells to ELF MF is modulated by

genes that are implicated in genetic instability syndromes [218]. Exposure to a pulsating

MF 50 Hz, 45 mT three times for three hours was found to protect U937 human lymphoid

cell lines against puromycin-induced cell death in a cell density-dependent manner (an

increased density induced cells death and prevented puromycin-induced cell death) [219].

The effects of 6- and 10-T SMFs was studied on HL-60 cells to evaluate the expression

of protooncogenes. It was found that exposure to a strong MF gradient induced c-Jun

expression, whereas no alteration of the expression of the

c-fos

and

c-myc

protooncogenes

was observed [220]. In the same cell line, exposure to LF EMF to lower intensity (5, 300,

and 500

µT)

did not affect calcium signaling [221].

An ELF magnetic ﬁeld was also found to inﬂuence the early development of mesenchy-

mal stem cells that were exposed for 23 days to 50 Hz and 20 mT intensity [222], whereas

no variations in surface morphology and cell death occurred between the control and

the exposed osteosarcoma human cells that were exposed to 50 Hz and 0.5 mT, although

signiﬁcant changes were noted in cell growth [223].

Supplementary Table S3 reports the effects of MFs on human cell lines. The type of

cell, response to MF, range and duration of MF exposure, materials and methods that were

used, and the main conclusions are reported.

3.2. Studies on Animals

Exposure to MF was found to be a cancer promoting factor in animal experiments;

animal studies are often used in the evaluation of suspected human carcinogens [224].

However, discrepancies in results were found to be associated with the use of different

sub-strains, different sources for diet, environmental conditions, and MF exposure met-

rics [225–227]. The continuous monitoring of both MF and other environmental parameters

is, therefore, an important part in the overall quality of the obtained results [228]. Experi-

ments with animals were also important to determine which genetic and environmental

factors are critical for potential carcinogenic or cocarcinogenic effects of ELF EMF expo-

sure [229]. Furthermore, the genetic background was found to play a pivotal role in effects

of MF exposure [230]. To study the effects of MFs on cancer cell growth, multiple exposure

levels have been performed using mice and rats as experimental models.

3.2.1. Studies on Mice

Several studies revealed that there were not signiﬁcant effects of MF on cancer in

mouse studies, failing to support the hypothesis that acute MF exposure causes DNA

damage. The experimental mice were injected with mammary murine adenocarcinoma to

investigate the interaction between a 50 Hz, 2 mT MF exposure and cell growth.

Neither the time of tumor cell injection nor the time of exposure produced differ-

ences between the unexposed, sham, and exposed mice [231] and no association between

exposure and the incidence of benign or malignant tumors was found in squamous cell

carcinomas of mice that were exposed to 60 Hz, 2 mT [232]. Also, the results do not sup-

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port the hypothesis that acute MF exposure causes DNA damage and apoptosis in the

cerebellums of immature mice that were exposed to 60 Hz and 1 mT for 2 h [233,234].

Exposure to 50 Hz and 500

µT

MF did not alter responses of inﬂammatory genes

and the activation of splenic lymphocytes in mice [235] and was not a signiﬁcant risk

factor for hematopoietic diseases, even when high exposure levels were used (50 Hz, 1 mT

for 7 days) [236]. In mice, as revealed in humans (see above), long-term and continuous

exposure to simulated powerline MFs (0.5–77

µT)

did not result in a decreased nocturnal

melatonin secretion [85].

The use of NI MFs has shown early promise in a number of animal studies as an

effective tool against many types of cancer (see also below). The effect of varying durations

of MF exposure on tumor growth and viability has been studied in mice that were injected

with breast cancer cells by using an in vivo imaging system. The results showed the

potential of MF in cancer therapeutics, either as adjunct or primary therapy [237].

Long-term exposure showed a signiﬁcant effect of MF on mice. When a parental

generation of six week-old OF1 mice was exposed to an artiﬁcial ELF MF (50 Hz, 15

µT)

for 14 weeks, activated partial thromboplastin time and reptilase time values signiﬁcantly

increased in the female mice, which also showed a very signiﬁcant shortening of the

prothrombin time that was associated with ELF MF exposure [238]. A chronic exposure of

mice to a 60 Hz and 110

µT

intensity was found to inﬂuence some hematologic parameters

and the weight of thee liver and also caused spleen hyperfunction [239]. Long-term

exposure to MF (50 Hz, 50

µT)

was found to be a signiﬁcant risk factor for neoplastic

development and fertility in C57BL/6NJ female mice and C3H/HeNJ male mice [240].

3.2.2. Studies on Rat

Both positive and negative effects of MF have been demonstrated in studies using

rats. Artiﬁcial MFs of 1 mT that was administered for seven weeks was not carcinogenic

nor cancer-promoting for colon carcinogenesis in male Sprague–Dawley rats [241]. No

overall effects of 60 Hz and 1 mT MF on splenomegaly or survival were found in the

exposed Fischer/344 rats. In addition, no signiﬁcant and/or consistent differences were

detected in the hematological parameters between the exposed and control rats [242]. MF

exposure for 14 weeks to 6.45

µT

did not appear to be a strong co-tumorigenic agent in

Sprague–Dawley female rats mammary, lung, and skin models [243]. On the other hand,

EMFs resulted in signiﬁcant alterations in cell adhesion mechanisms when histological,

immunohistochemical, and histopathological analyses were performed on Wistar albino

rats that were exposed for six months to 5 mT MFs [244]. MF exposure (50 Hz, 100

µT

for

2–26 weeks) of female Sprague–Dawley rats resulted in an increased proliferative activity

of the mammary epithelium which was associated with the cocarcinogenic or tumor-

promoting effects of MF exposure that was observed in the 7,12-dimethylbenz(a)anthracene

model of breast cancer [89], and mammary tumorigenesis [245].

The Supplementary Table S4 reports the effects of MFs on both mice and rats. The type

of cell, response to MF, range and duration of MF exposure, materials and methods used,

and the main conclusions are reported. Figure

summarizes the in vivo and in vitro effects

of magnetic ﬁelds on cancer from studies on human cells (including cell-free systems) and

in animals (mice and rat).

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Figure 4. Summary of the in vivo and in vitro effects of MFs on cancer from studies on human and

Figure 4.

Summary of the in vivo and in vitro effects of MFs on cancer from studies on human and

animal cells. Different treatments (e.g., strength, duration, frequency, etc.) induce signal transduc‐

animal cells. Different treatments (e.g., strength, duration, frequency, etc.) induce signal transduction

tion pathways that eventually trigger gene expression. In vitro studies show significant effects on

pathways that eventually trigger gene expression. In vitro studies show signiﬁcant effects on cell

cell cycle, proliferation, and apoptosis. Human cells have been used to evaluate the effect of MF on

cycle, proliferation, and apoptosis. Human cells have been used to evaluate the effect

several cancer types, whereas animal experimentation has been focused on mice and rats.

of MF on

several cancer types, whereas animal experimentation has been focused on mice and rats.

3.3. Involvement of Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS)

ROS (such as superoxide [O

2•−

] and hydrogen peroxide [H

]) and RNS (e.g., nitric

•−

ROS

•

]) are generated during the oxidative cell metabolism. The cellular oxidative

oxide [NO

(such as superoxide [O

] and hydrogen peroxide [H

]) and RNS (e.g., nitric

oxide [NO

•

]) are generated during the oxidative cell metabolism. The cellular oxidative

stress depends on the balance between the production of ROS and RNS and the activity

of the antioxidant system. Excessive ROS/RNS, which is caused by the deregulated redox

of the antioxidant system. Excessive ROS/RNS, which is caused by the deregulated

homeostasis, is a hallmark of disease [246]. Free‐radical‐scavenging enzymes, such as cat‐

redox homeostasis, is a hallmark of disease [246]. Free-radical-scavenging enzymes, such

alase (CAT), glutathione peroxidase (GPX), and superoxide dismutase (SOD), are the first

as catalase (CAT), glutathione peroxidase (GPX), and superoxide dismutase (SOD), are

line of defense against oxidative injury [247]. After EMF exposure, significant variations

the ﬁrst line of defense against oxidative injury [247]. After EMF exposure, signiﬁcant

in the total antioxidant activity; vitamin E and vitamin A concentrations; increase of

malondialdehyde (MDA, a product of polyunsaturated fatty acid peroxidation which is

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variations in the total antioxidant activity; vitamin E and vitamin A concentrations; increase

of malondialdehyde (MDA, a product of polyunsaturated fatty acid peroxidation which

is used as an indicator of oxidative stress in cells and tissues); and plasma selenium

concentration in erythrocytes have been observed [248]. A recent review concluded that

most animal and many cell studies show increased oxidative stress that is caused by

MF [249]. Free-radical formation and the consequences of their effects on living systems

explains the increased cancer risks that are associated with mobile phone use, occupational

exposure to MF, and residential exposure to power lines [250].

ROS may be involved in RP reactions that have been considered as one of the mecha-

nisms of transduction that is able to initiate EMF-induced oxidative stress [19]. It is known

that applied MFs and magnetic isotope substitution can alter the rates and product yields

of free-radical reactions with the formation of transient paramagnetic intermediates in

non-equilibrium electron spin states. The most common source of spin-chemical effects

are organic RPs. Typically formed in a singlet (S) or a triplet (T) state by a reaction that

conserves electron spin, RPs interconvert coherently between their S and T states as a

result of the Zeeman, hyperﬁne, exchange, and dipolar interactions of the electrons and the

nuclear spins to which they are coupled [251]. Applied MFs alter the extent and timing of

the S T interchange and hence the yields of products that are formed spin-selectively from

the S and T states [252]. MFs and spin effects have proven to be useful mechanistic tools

for radical mechanism in biology [252] and the RP mechanism (RPM) has been associated

with increased levels of ROS [253]. Moreover, the role of cryptochromes as the putative

magnetosensitive molecules in magnetoreception has been considered in the RPM and

discussed as related to cancer-relevant biological processes [254]. The results are also

consistent with MF effects on light-independent radical reactions [255].

Signiﬁcant increases in ROS levels have been found to inﬂuence the hepatic redox

state [256] and were observed in several cell lines just after the end of ELF EMF expo-

sure [213,257–265]. ELF EMF exposure also elevates the expression of RNS and O

•−

which are countered by compensatory changes in antioxidant CAT activity and enzymatic

kinetic parameters that are related to cytochrome P450 (CYP-450) and CAT activity [266].

Moreover, the modulation in kinetic parameters of CAT, CYP-450, SOD, and MDA concen-

tration and iNOS enzymes in response to ELF EMF [267–269] and the negligible effects on

GPX [270] indicates an interaction between the ELF EMF and the enzymological system.

SMF promoted the release of ferrous iron (Fe

) and induced the production of ROS in

osteosarcoma stem cells [271]. Superoxide increased in the micronucleus and mitochondria

with an exposure-response relationship and cytosolic superoxide increased at 10

µT

SH-SY5Y and C6 cells [272]. These results conﬁrm that the threshold for biological effects

of ELF MFs may be as low as 10

µT.

In liver tissue of female rats, long-term ELF-MF exposure enhanced the oxidative/

nitrosative stress and might have a deteriorative effect on cellular proteins rather than

lipids by enhancing 3-nitrotyrosine formation [273]. MF appears to induce apoptosis

effects through oxidative stress [274] and mitochondrial depolarization [275], whereas the

inﬂuence of correlations between ELF EMF and vitamin E supplementation have been

shown on antioxidant enzyme activity in AT478 malignant cells in vitro [276]. In embryonic

stem cell-derived embryoid bodies, exposure to 10 mT MFs increased ROS [277].

Supplementary Table S5 reports the effects of MFs on ROS and RNS. The type of

cell/tissue/organ, response to MF, range and duration of MF exposure, materials and meth-

ods used, and the main conclusions are reported. Figure

summarizes the involvement of

ROS and RNS in the cellular and organismic responses to MFs.

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Figure 5. Summary of the involvement of ROS and RNS in the cellular and organismic responses to

Figure 5.

Summary of the involvement of ROS and RNS in the cellular and organismic responses

MFs. There are three major effects of varying MF the are reported. MFs alter the redox status of the

to MFs. There are three major effects of varying MF the are reported. MFs alter the redox status of

cell by affecting the activity and gene expression of ROS‐scavenging systems, including CAT, SOD,

the cell by affecting the activity and gene expression of ROS-scavenging systems, including CAT,

GPX, vitamins, and monooxygenases. Membrane degradation is evidenced by MDA detection. On

SOD, GPX, vitamins, and monooxygenases. Membrane degradation is evidenced by MDA detection.

the other hand, MFs trigger the production of ROS and NOS and early events involving the radical

On the other hand, MFs trigger the production of ROS and NOS and early events involving the

pair mechanism and spin‐chemical effects. The altered oxidative status eventually induces the ex‐

pression of oncogenes. The alteration of the oxidative status is also evident at the subcellular, cellu‐

radical pair mechanism and spin-chemical effects. The altered oxidative status eventually induces

lar, tissue, and organ level.

The alteration of the oxidative status is also evident at the subcellular,

the expression of oncogenes.

cellular, tissue, and organ level.

4. Magnetic Fields and Cancer Theranostics

So far, we have discussed the possibility that exposure to MFs may be correlated with

So far, we have discussed the possibility that exposure to MFs may be correlated

cancer. However, MFs have been widely used for cancer diagnostic and therapeutic ap‐

with cancer. However, MFs have been widely used for cancer diagnostic and therapeutic

plications. In this section, the use of MRI as a cancer diagnostic and therapeutic method

applications. In this section, the use of MRI as a cancer diagnostic and therapeutic method

and the use of magnetic nanoparticle for cancer treatment will be discussed.

4.1. Magnetic Fields and Cancer Diagnosis

MRI, a medical application of nuclear magnetic resonance (NMR), uses strong MFs,

MF gradients, and radio waves to generate images of the organs in the body [278]. There

is a trade‐off between MF dose effects and the image quality of MR‐guided radiotherapy

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is a trade-off between MF dose effects and the image quality of MR-guided radiotherapy

systems and the MF strength may affect the severity of MF dose effects [279], such as

the electron return effect [280]. MRI has also been used in combination with positron

emission tomography (PET) and has a strong potential clinical use for the imaging of

several forms of cancer [281]. Using ultrasmall superparamagnetic particles of iron oxide

(USPIO), it is possible to enhance the power of MRI for noninvasive diagnostics of different

types of cancer [282,283] (see also 4.2). Since alterations in the Na

ion concentration

may potentially be used as a biomarker for malignant tumor diagnosis and prognosis,

Na-23-magnetic resonance imaging (Na-23-MRI) was found to have potential as a direct

noninvasive in vivo diagnostic and prognostic biomarker for cancer therapy, particularly in

cancer immunotherapy [284]. The potential role of MRI in the detection of several cancers

will be discussed in the next paragraphs.

4.1.1. Brain and Glioma Cancer

MRI has allowed the characterization and diagnosis of human brain cancers in spatial

and volumetric analysis [285], as a substitute for biopsies [286], in glioma genotyping [287],

brain cancer classiﬁcation [288,289], as a non-invasive tool for simultaneous and automated

tumor segmentation [290], and to investigate the early stages of slow-growing invasive

tumors [291–294]. MRI is used before treatment and at the end of treatment or disease

progression [295,296] and to assess neurological complications of cancer treatment [297].

PET/MRI is used in brain tumor grading and staging [298,299] as a diagnostic and therapeu-

tic strategy for glioma [300] and for improved diagnostic and therapeutic assessment in pe-

diatric, teenage, and young adult brain tumors [301].

C–methyl-L-methionine (C-11-MET)

PET/MRI was found to improve the diagnostic accuracy to differentiate treatment-related

changes from true progression in recurrent glioma [302] and was useful for the assessment

of isocitrate dehydrogenase (IDH) status [303]. Perfusion MRI is used to differentiate

glioma from brain metastasis [304], whereas dynamic glucose-enhanced (DGE) MRI is a fea-

sible technique for studying brain tumor enhancement reﬂecting differences in tumor blood

volume and permeability with respect to a normal brain [305]. MRI-coupled ﬂuorescence

molecular tomography (MRI-FMT) determines epidermal growth factor receptor status in

brain cancer [306] while advanced MRI techniques contribute to biological and imaging

features of glioma and immune system interactions [307] and in the clinical management

of adult gliomas [308]. The application of gadoﬂuorine-M (GfM) results in superior delin-

eation of experimental glioma compared with conventional MRI techniques [309], whereas

the labeling tumor cells with superparamagnetic iron oxide (SPIO) and performing an

MRI scan dynamically monitors the development and biological behavior of glioma at

a very early stage [310]. Vascular, extracellular, and restricted diffusion for cytometry in

tumors MRI has been used as a potential test for diagnostic stratiﬁcation to investigate

the tissue microstructure in glioma [311], whereas dynamic contrast-enhanced (DCE) MRI

detects increases in gadolinium (Gd)-enhancement of brain tumors [312] and provides an

unambiguous indication of the brain tumor photodynamic therapy outcome [313]. The

enhancement on MRI may assist in identifying HER2 overexpression in breast cancer brain

metastases [314].

4.1.2. Head and Neck Cancer

According to the National Cancer Data Base, head and neck cancer accounted for

6.6% of all new cancers [315]. The use of PET and MRI, separately or combined, has

been successful for assessing the metastatic lymph nodes in patients with head and neck

cancer and offers advantages in staging with regard to increased anatomical details and

radiation dose reduction [316–318]. Diffusion-weighted imaging (DWI) and intravenous

(IV) contrast T1 dynamic perfusion imaging are a valid support for the functional MRI of

tumors of the head and neck [319–322] and algorithms for automatic head and neck 3D

tumor segmentation from MRI have been developed [323]. For head and neck cancer, MRI-

guided radiotherapy achieves clinical outcomes that are comparable to contemporary series

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reporting on intensity-modulated radiotherapy (IMRT) [324] and the use of targeted 3 T

MRI was found to be useful for deﬁning the presence and extent of large nerve perineural

spread in head and neck cancers [325]. Retrospective image fusion of PET/MRI for the

assessment of the extent of the primary tumor (T stage) and metastasis to regional lymph

nodes (N stage) has been evaluated [326], whereas USPIO-enhanced MRI in patients with a

clinical neck cancer was able to differentiate borderline-sized lymph nodes [327].

4.1.3. Thyroid Cancer

Thyroid cancer is one of the fastest growing cancer diagnoses worldwide [328]; it is

the most common endocrine malignancy in children [329], it is three times more frequent

in females [330], and poorly differentiated and anaplastic thyroid carcinomas represent a

challenge to physicians on the basis of the current cancer treatment modalities [331]. MRI

shows sensitivity and speciﬁcity to diagnose recurrent thyroid carcinomas [332], thyroid

cancer cervical lymph node metastases [333], papillary carcinomas [334], and is more sensi-

tive than ultrasonography in detecting central compartment metastases in papillary thyroid

carcinoma [335]. However, MRI is inadequate for the detection of metastases in small

lymph nodes [336]. Esophageal invasion by thyroid carcinoma was accurately predicted

with MRI [337], whereas MRI, DWI-MRI, and MRI-based computer-aided diagnosis (CAD)

allow the differentiation of thyroid nodules whether benign or malignant [338–340] and

for the detection of inner and outer thyroid lamina invasion [341]. PET/MRI of the neck is

superior to PET-computed tomography (CT) in detecting iodine-positive lesions [342] and

provided further information in an overwhelming majority of thyroid cancer patients [343]

4.1.4. Breast Cancer

Among current clinical imaging modalities, MRI of the breast has the highest sensitiv-

ity for breast cancer detection and is becoming an indispensable tool for breast-imaging

procedures [344,345]. Multi-parametric (Mp) MRI is the most sensitive imaging modal-

ity for breast cancer detection [346] and has been successfully used in combination with

PET/MRI [347] and other imaging modalities. Abbreviated breast MRI uses only a select

number of sequences and postcontrast imaging reduces the table time and reading time to

maximize availability, patient tolerance, and accessibility [348], and may enable the more

widespread use of breast-MRI for screening [349].

4.1.5. Lung Cancer

Among all cancer, lung cancer has the highest rate of mortality in the western

world [350]. MRI is suitable for lung cancer screening with an excellent sensitivity and

speciﬁcity of malignant as well as calciﬁed and subsolid nodules [351–355] and for radia-

tion treatment planning in lung cancer [356]. DWI-MRI protocol have been designed for

imaging malignant lung tumors, achieving satisfactory within-patient repeatability [357],

while recent advances in PET/MRI for lung cancer staging have been reviewed [358,359].

4.1.6. Gastric Cancer

Gastric cancer is an important healthcare problem from a global perspective, being the

ﬁfth most common malignancy and the third leading cause of cancer-related death [360].

Most of the cases are diagnosed at late stages when the treatment is largely ineffective,

and MRI is of great value in patients with gastric cancer [361,362]. MRI is useful for

distant metastasis assessment with particular reference to peritoneal and liver metastases

assessment [361]. DWI-MRI and apparent diffusion coefﬁcient (ADC) values showed to

be useful in preoperative MRI staging of gastric cancer [363,364], but has a low accuracy

to detect or to differentiate metastatic and non-metastatic lymph nodes based on their

ADC values in gastric cancer [365]. MRI is useful for the diagnosis of serosal invasion

of gastric cancer [366], in the diagnosis of regional lymph node metastases [367], and is

the best method in the assessment of gastric wall inﬁltration in gastric cancer [368]. MRI

is more accurate in achieving adequate staging results [369,370] and in the evaluation of

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T-stage than CT [371,372]. DCE-MRI parameters of gastric cancers may provide prognostic

information [373,374], whereas multiparametric fully integrated

ﬂuordesoxyglucose

([F-18]-FDG)-PET/MRI may improve the diagnostic accuracy for translational gastric

cancer research [375], for preoperative M staging as well as the resectability of gastric

cancers compared to multi detector computed tomography (MDCT) [376]. Compared with

PET/CT, PET/MRI performs more accurately in TNM staging and is optimal for accurate

N staging [377], whereas high-resolution MRI (HR MRI) has good diagnostic performance

and may serve as an alternative technique in the T staging of patients with esophagogastric

junction cancer in clinical practice [378]. The preoperative prediction results of MRI are

consistent with postoperative pathological results [379], although the clinical use of MRI

for gastric cancer is still under discussion [380].

4.1.7. Pancreatic Cancer

Pancreatic cancer is a deadly disease, mainly because it is very resistant to chemother-

apy and radiation therapy and is generally discovered very late [381]. MRI provides

relevant information for the diagnostic evaluation of malignant pancreatic tumors [382,383]

by predicting the survival in advanced pancreatic cancer patients [384]. The use of MRI of

the liver for the initial staging of pancreatic cancer results in lower total costs and higher

effectiveness [385]. MRI and CT show similar performance in the presurgical evaluation

of pancreatic cancer [386,387]. Preoperative MRI is instrumental to detect the stage and

resectability of pancreatic cancer [388] and preoperative gadoxetic acid-enhanced liver MRI

has a high diagnostic performance in detecting liver metastasis from pancreatic ductal

adenocarcinoma [389,390]. Gadolinium-enhanced MRI with DWI detected synchronous

liver metastases [391], whereas [F-18]-FDG PET/MRI provides an imaging tool to im-

prove the staging of pancreatic cancer and for the identiﬁcation of Sister Mary Joseph

nodules [392]. However, the addition of DWI to conventional MRI does not facilitate

the differentiation of pancreatic cancer from chronic pancreatitis [393], whereas MRI can

differentiate pancreatic carcinoma from chronic pancreatitis successfully when including

Gd-enhanced T1-weighted 3D-GE sequences [394]. MRI-guided celiac plexus neurolysis

is an effective and minimally invasive procedure for the palliative pain management of

pancreatic cancer [395], whereas dynamic susceptibility contrast MRI (DSC-MRI) may

predict early progression in patients with advanced pancreatic cancer that are undergoing

chemotherapy [396]. MRI has been used to monitor radiofrequency heat (RFH)-enhanced

chemotherapy in pancreatic cancers for the efﬁcient treatment of pancreatic malignancies

using MRI/RFH-integrated local chemotherapy [397].

4.1.8. Hepatocellular Carcinoma

Primary liver cancer is the second most common cause of cancer mortality worldwide

and the sixth most common cancer overall [398]. MRI is superior to CT in sensitivity, speci-

ﬁcity, and accuracy [399] and can be used to determine the differential diagnosis [400–402],

variant analysis [403], arterial phase hyperenhancement [404], small precursor and recur-

rent lesions [405,406], liver perfusion [407], histological grade, microvascular invasion

status, local and systemic therapeutic responses, prognosis [408,409], and as a preoperative

marker [410] in hepatocellular carcinoma patients. PET/MRI imaging is also used for

the diagnosis of patients with hepatocellular carcinoma [411–413], whereas multi-phasic

MRI staging was found to be more accurate than the straight hepatocellular carcinoma-

grading approach [414]. Texture analysis that was based on gadolinium-ethoxybenzyl-

diethylenetriamine penta-acetic acid (GdEOB-DTPA)-enhanced MRI is used for early predic-

tion of therapeutic outcome in intermediate hepatocellular carcinoma [415], identiﬁcation of

vessels encapsulating tumor clusters-positive hepatocellular carcinoma [416], has a higher

diagnostic rate and a better diagnostic value in small hepatocellular carcinoma [417], and

for the detection of capsule appearance in patients with hepatocellular carcinoma [404] and

liver cirrhosis [418,419]. DCE-MRI is used in the prediction of staging B or C hepatocellular

carcinoma [415] and for the quantiﬁcation of perfusion metrics [420] with a superior modal-

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ity for diagnosis compared with dynamic contrast-enhanced CT-scan [421]. DWI-MRI

is the gold standard in detecting recurrent lesions [405], monitoring response to therapy,

predicting response, assessing prognosis, and distinguishing tumor recurrence from the

treatment effect [422]; however, DWI adds little value to MRI in target delineation [423].

4.1.9. Gallbladder Carcinoma

The most common cancer of the biliary system is gallbladder carcinoma [424]. MRI is a

useful imaging tool for the staging, diagnosis, and evaluation of the treatment response and

provides superior soft-tissue characterization of the gallbladder and biliary tree [425,426].

DWI is the preferred imaging technique for discriminating benign from malignant disease

in gallbladder cancer [427,428].

4.1.10. Renal Cancer

Renal cancer (that is neoplasia of the kidney, renal pelvis, or ureter (ICD-9 189 and

ICD-10 C64-C66)) is the seventh most common malignancy in men [429]. There are three

main risk factors for cancer of the kidney: age, smoking, and obesity [430]. Renal carcinoma

is often ﬁrst detected and characterized with imaging, with CT and MRI being the most

common modalities that are used for diagnosis, staging, and surveillance [431–433]. MRI

differentiates papillary renal cell carcinoma from other renal masses [434], whereas MRI

and normalized ADC has utility in differentiating central renal cell carcinoma from renal

pelvic urothelial carcinoma [435]. DCE MRI allows an estimation of the grading of renal

cell carcinoma [436] and along with DWI MRI and multiphase contrast-enhanced MRI

(MCE-MRI) contributes with prognostic information, even at baseline scans, by predicting

the tumor response early after initiating therapy [437].

4.1.11. Bladder Cancer

Bladder cancer is the fourth most common cancer worldwide [438]. MRI is effective

in bladder cancer staging as well as differentiating superﬁcial from invasive tumors and

organ-conﬁned from non-organ-conﬁned tumors [439–447]. MRI has shown potential

for the detection of muscle invasion [448]. Mp-MRI has been a useful modality for the T

staging of bladder cancer for clinical and research applications [449,450], whereas DCE-MRI

provides response biomarkers in clinical trials in subjects with primary bladder cancer [451].

For bladder cancer patients, diagnostics that are based on the use of hybrid systems

incorporating both MRI scanning capabilities with the linear accelerator offers a number

of potential advantages [452], whereas in bladder cancer patients that are undergoing

cystectomy, DWI is used in the detection of metastatic pelvic lymph nodes [453] and in the

preoperative T staging of urinary bladder cancer [454].

4.1.12. Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy; it accounts for 2.5% of

all malignancies among females but 5% of female cancer deaths because of low survival

rates that are largely driven by late-stage diagnoses [455]. An MRI of ovarian cancer has

been instrumental to differentiate metastatic ovarian tumors from primary epithelial ovar-

ian cancers [456]. Functional MRI techniques such as tumor-selective molecular imaging

(TSMI), DW-MRI, and DCE-MRI are under evaluation as possible predictive and prognostic

biomarkers in the context of ovarian malignancy and in routine clinical practice [457–461].

Contrasting results are reported about the role of PET/MRI in ovarian cancer [462]. MRI

was found to be more sensitive than PET/CT for detecting local pelvic recurrence and

peritoneal lesions of recurrent ovarian tumors [463,464], although PET/CT had a higher

speciﬁcity than pelvic MRI for diagnosis of malignant ovarian tumors [465–467]. A consen-

sus process in the creation of a standardized lexicon for ovarian and adnexal lesions for

MRI and the resultant lexicon has been recently published [468].

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4.1.13. Cervical Uterine Cancer

Cervical uterine cancer is the leading cause of morbidity and mortality in women in

developing countries and is known to be related to human papillomavirus [469]. Pelvic

MRI is the reference examination for the evaluation of cervical cancers, allowing the accu-

rate evaluation of tumor size, parametrial extension, and lymph node metastasis, which

are essential factors for therapeutic management [470–473]. MRI, CT/MRI, and PET/MRI

have been used for cervical cancer staging and lymph node metastasis [474,475], and

PET/MRI was found to possess a higher diagnostic sensitivity [476], speciﬁcity [477], and

accuracy [478,479], also during pregnancy [480], being helpful in clinical diagnosis [481],

prediction [482], and treatment [483,484]. MRI diagnosis is an auxiliary method for cervical

cancer treatment when used in combination with tumor markers (e.g., squamous cell carci-

noma antigen) [485] and for the management of women with early cervical cancer [486].

DWI-MRI and ADC are used as a non-invasive imaging methods for characterizing the

fraction of collagen I-positive tissue across different tumor models of uterine cervical

cancer [487], for the pathological grade of tumor [488], for the differentiation between

metastatic and non-metastatic pelvic lymph nodes [489], and between normal and can-

cerous tissue in the uterine cervix [490]. Another noninvasive technique that is used to

assess tumor vascular oxygenation at 3 T in cervical cancer staging is blood oxygenation

level-dependent contrast MRI [491].

4.1.14. Endometrial Cancer

Endometrial cancer is the fourth most common malignancy in women and the most

common gynecological malignancy in the developed world after lung, colorectal, and

breast cancer [492]. MRI is recommended for the initial staging and report of endometrial

cancer [493–496] and preoperative pelvic MRI is a moderately sensitive and speciﬁc method

of identifying invasion to the outer half of myometrium in endometrial cancer [497–499].

MRI has a high speciﬁcity and negative predictive value in endometrial cancer staging [500];

however, its accuracy in detecting myoinvasion is limited [501]. MRI with DWI and DCE

sequences can help establish a correct diagnosis [502,503], while 3.0 T multimodal MRI is an

important imaging tool for preoperative endometrial cancer staging [504]. MRI quantitative

assessments such as tumor area ratio (TAR), tumor volume ratio (TVR), MRI-based whole-

tumor radiomic signatures, and ADC were found to improve the accuracy of preoperative

staging, helping in the risk stratiﬁcation of endometrial cancer [505–507]. The combination

of MRI and immunohistochemical examination is a powerful tool for preoperative risk

stratiﬁcation to assist in clinical decision-making for endometrial cancer patients [508].

[F-18]-FDG PET/MRI is a valid imaging technique in patients with endometrial cancer,

both in staging and restaging as an alternative diagnostic strategy to conventional imaging

modalities, also considering the limited radiation exposure [509–511], whereas integrated

PET/MRI successfully assesses the lymph node metastasis and myometrial invasion in

patients with endometrial cancer [512,513]. MRI-guided intensity-modulated radiation

therapy has been used for locally recurrent endometrial cancer after resection [514].

4.1.15. Prostate Cancer

Prostate cancer is the most common cancer for males, and it is estimated that 15% men

are predicted to develop prostate cancer over their lifetime [515]. The application of MRI

has been successfully used for its sensitivity in detecting clinically signiﬁcant cancer and the

ability to localize the tumor within the prostate gland [516,517] by using Mp-MRI [518–522]

and in the hybrid PET/MRI [523–525]. In addition to the fusion strategy, biopsies with

MRI targets play an important role in the assessment of patients with a previous negative

prostate biopsy [518].

4.1.16. Testicular Cancer

In men between 15 and 49 years-old, the most common nonhematologic malignancy

is testicular cancer [526]. It has excellent cure rates; however, poor guideline adherence

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can lead to inappropriate management with a detrimental effect on outcomes [527]. MRI

is successful in the diagnosis of testicular germ cell cancer [528,529], particularly when

directed towards the retroperitoneum and pelvis only [530]. Functional information that is

based on DWI and DCE MRI data improve testicular mass lesion characterization [531,532]

and can be used to characterize small, solid testicular tumors [533] and for the follow-

up of testicular cancer patients [534], independently of the examiner [535]. Mp-MRI can

potentially differentiate benign stromal tumors from malignant testicular neoplasms, which

can help to avoid radical orchiectomy [536,537]. MRI can be used as an alternative to CT to

reduce radiation exposure [538,539] and is a valuable diagnostic aid in the preoperative

localization of ectopic testes in cryptorchidism or if ﬁndings are equivocal [540].

4.1.17. Colorectal Cancer

Colorectal cancer is a common cancer and a common cause of death. There is evi-

dence that an important proportion of colorectal cancer patients remain untreated [541].

Pelvic MRI is used for the local of T and N staging of rectal cancer and has the advan-

tage of improved patient comfort [542], improved reproducibility and accuracy [543],

reduced care costs [544], and for completeness and better understanding of related pelvic

anatomy [545–547]. The use of DWI with ADC value in addition to conventional MRI yields

better diagnostic accuracy than using conventional MRI alone in detection, correlation

with tumor histologic grade, and the initial staging in patients with locally advanced col-

orectal cancer [548,549]; however, DW-MRI is inferior to [F-18]-FDG-PET for the detection

of primary lesions but superior for the detection of lymph node metastases [550]. MRI

shows moderate sensitivity and good speciﬁcity for the detection of extramural venous

invasion (EMVI) in colorectal cancer [551,552], while 3D colorectal MRI gives better and

accurate segmentation results than 3D fully convolutional neural networks alone [553]. For

lymph node metastasis of colorectal cancer, the sensitivity and speciﬁcity of preoperative

diagnosis by diffusion (D-MRI) is higher if the node is hyperintense and more than 9 mm

in diameter [554], whereas high-b-value DWI-MRI has a high sensitivity and speciﬁcity to

detect colorectal adenocarcinoma [555]. Recent developments and emerging technologies

in CT and MRI are changing the management of colorectal cancer patients in many clinical

scenarios [556]. MRI is more accurate than CT [557] and MDCT for the evaluation of liver

metastases [558], whereas for patients with colorectal cancer, PET/MRI may aid in the

selection of more appropriate treatment strategies [559]. Whole-body MRI (WB-MRI) is a

radiation-free alternative to standard sequential algorithms of staging and follow-up of

colorectal cancer [560].

4.2. Magnetic Fields and Cancer Treatment

Radiation therapy, chemotherapy, and immunotherapy, alone or in combination with

therapies such as photothermal therapy, photodynamic therapy, hyperthermia, and radio-

therapy have been proposed in the recent literature [561]. In the next section, the use of

magnetic nanoparticles for the delivery of anticancer agents and magnetomechanical tools

and in hyperthermia will be discussed.

4.2.1. Delivery of Anticancer Agents via Magnetic Carrier Particles

An exciting new prospect in treating cancer is the delivery of anticancer agents via

magnetic carrier particles, which are used as a "carrier system" for a variety of anticancer

agents [562–565]. For instance, by using an external MF, it is possible to guide magnetic iron

oxide nanoparticles (MIONs) to their target. This is the principle behind the development of

superparamagnetic iron oxide nanoparticles (SPIONs) as novel drug delivery vehicles [566].

Palmitoyl chitosan that is co-encapsulated with SPIONs and the anticancer drug pacli-

taxel via the nanoprecipitation process increased the amount of drug in cancer cells [567],

whereas doxorubicin (Dox)-conjugated heparin was used with the SPION technology for

targeted anticancer drug delivery [568]. In MCF-7 breast cancer cells, Dox was rapidly

internalized and exhibited higher toxicity than treatments with Dox alone when it was

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assembled in magnetic nanoparticle-supported lipid bilayers [569] and with Dox-loaded

polymer@Au/Fe

core/shell nanoparticles in simulated cancerous environments [570].

Bioactive molecules such as curcumin can be loaded in magnetic silk ﬁbroin core-shell

nanoparticles to enhance cytotoxicity and cellular uptake in the human Caucasian breast

adenocarcinoma cell lines with superior biomedical applications due to their size range,

which is particularly desired for cell internalization [571].

It has to be considered that since the magnetic force decreases rapidly with the distance

from the magnets, the targeting of tumors that are situated at large distances from the

surface of the human body might be difﬁcult. Therefore, the delivery of anticancer agents

via magnetic carrier particles appears to be more suitable for treating sub-surface cancers

within the human body [572]. Nevertheless, several new magnetic nanoparticles have

been designed and evaluated for cancer treatment, offering the ability to deliver drugs

efﬁciently [573–575].

Despite the increasing body of evidence supporting promising results, there are some

drawbacks that are related to magnetic nanoparticles (MNP) use in drug delivery, such as

the difﬁculty in maintaining the therapeutic action in three dimensions inside the human

body, the limitation to maintain efﬁcacy in the target organ once the MF is removed from

outside, and the limited effective incorporation of magnetic iron oxide nanoparticles into

biomedical systems [576,577].

4.2.2. Magnetomechanical Methods for Cancer Therapy

Magnetomechanical therapy is one of the most prospective directions in tumor micro-

surgery based on a physical nanostructure that is able to transform the magnetic moment

to mechanical torque and a ligand molecule that allows the scalpel to precisely target tumor

cells [578]. Nano-magnetomechanical activation (NMMA) of the MNPs is used to localize

and apply force to target biomolecular structures as transport vesicles, cell organelles, en-

zyme molecules, etc., without signiﬁcant heating [579]. Nanospinners can exert mechanical

forces under a rotating magnetic ﬁeld at 15 Hz and 40 mT to target the mitochondria of

cancer cells [580]. Iron nanowires that are functionalized with anti-CD44 antibodies have

been used in a combination therapy that included magnetomechanical and photothermal

treatments on colon cancer cells [581]. Hedgehog-like microspheres that were composed

of needle-like MNP with carbon and gold double shells seriously damaged cancer cells

and strongly inhibited tumor growth through mechanical force [582]. Magnetic disks

are a new generation of MNP with outstanding properties to face biomedical challenges

in cancer treatment microsurgery [578] and the investigation toward the most efﬁcient

magnetomechanical actuator to destroy cancer cells has been recently reviewed [583].

4.2.3. Magnetic Hyperthermia Ad Cancer Treatment

Magnetic hyperthermia treatment (MHT) utilizes heat that is generated by MNPs

under an alternating MF to selectively kill tumor cells [584–586]. When exposed to an

alternating magnetic ﬁeld (AMF), MNPs can generate heat via hysteresis loss (large multi-

domain MNPs) or through Neel- and Brownian relaxation losses (typically small, single-

core MNPs) [587]. The efﬁciency of MHT depends on the size, concentration, solution

viscosity, and composition of MNPs as well as the strength and frequency of the MF [588].

Several materials are used to prepare MNP for MHT. Ferrimagnetic glass-ceramic have

been successfully used as thermo-seeds for a hyperthermic treatment of carcinoma cells in

Sprague–Dawley rats [589], whereas magnetite cationic liposomes where used to generate

hyperthermia on local tumors and lung metastases in a mouse model of osteosarcoma [590].

Spinel ferrite nanoparticles were successfully synthesized and used for MHT [591], whereas

MIONs (such as crack-free ferrimagnetic maghemite,

γ-Fe

) may be useful for the in

situ hyperthermic treatment of cancer [592–595]. SPIONs have been increasingly studied

for their excellent MHT applications [596,597], whereas lanthanum-strontium manganite

particles that were embellished with gold nanoparticles were found to be suitable for the

treatment of deeper tumors [598].

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The limitations and advantages to more effective clinical use of MNP-based ther-

mometry to achieve greater impact on clinical translation of MNH have been recently

reported [599]. Despite the wide use of MHT in clinical application, the technology suffers

from inadequate and uneven heating due to low and heterogeneous concentrations of

MNPs within the target tumor [600,601]. It has been calculated that, to achieve sufﬁcient

hyperthermia in targeted tumors, a high concentration of MNP is required [602] and often

the particle concentration is below that which is needed to induce sufﬁcient heating of

tissue, thus lowering the therapeutic effects of MHT [603].

The use of MNPs in “traditional” biomedical applications that are related to cancer

theranostics, such as drug delivery, hyperthermia, MRI, micro nuclear magnetic reso-

nance, and surface-enhanced Raman spectroscopic detection technology has been demon-

strated [604–609], including the development of next-generation high-performance thera-

nostic agents that are based on MNP assemblies [610]. Figure

summarizes the use of MFs

Int. J. Mol. Sci. 2022, 23, x FOR PEER REVIEW

28 of 54

in cancer theranostics.

Figure 6.

MFs and

MF are used in MRI for the diagnosis of several cancers, whereas the use of magnetic nanoparticles

therapy.

cancer theranostics. Theranostics combines the terms diagnostics and

for cancer therapy encompasses three major areas: magnetic hyperthermia that is aimed to kill can‐

MF are used in MRI for the diagnosis of several cancers, whereas the use of magnetic nanoparticles

cer cells with heat; drug delivery by the use of SPIONs and MIONs; and the exploitation of MNP

for cancer therapy

mechanical forces for application in nano and microsurgery.

hyperthermia that is aimed to kill

encompasses three major areas: magnetic

cancer cells with heat; drug delivery by the use of SPIONs and MIONs; and the exploitation of MNP

mechanical forces for application in nano and microsurgery.

Figure 6. MFs and cancer theranostics. Theranostics combines the terms diagnostics and therapy.

Int. J. Mol. Sci.

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5. Conclusions

The potential health effects of man-made EMF have been a topic of scientiﬁc interest

since the late 1800s and have received particular attention during the last 40 years. Since the

ﬁrst studies suggesting a relationship between MF and childhood cancer [29], the scientiﬁc

community has evaluated the possible mechanisms for the effects of MFs on biological

systems. Epidemiological studies are often controversial and sometimes misleading. Nev-

ertheless, there is a consensus on the positive relationship between residential/domestic

exposure to ELF EMF and the occurrence of brain cancer, whereas contrasting results

require more experimentation to assess the inﬂuence of occupational exposure to MFs on

brain cancer. The epidemiology of leukemia as related to ELF EMF in adults is controversial

in both residential/domestic and occupational exposure. For children, leukemia is not

associated to occupational exposure, whereas a growing body of evidence indicates a corre-

lation between residential/domestic exposure to ELF EMF and childhood leukemia. Breast

cancer has been related to ELF EMF exposure more in residential/domestic epidemiological

studies than in occupational, but the melatonin hypothesis, although recently revisited,

ﬁnds little consensus. When studied at the cellular and in vitro level, MFs exert their effect

on both human and animal (rat and mice, mainly) cells when used at a high intensity and

for a long time. The common response is the production of ROS, which trigger a cascade

of other cellular responses which might be the direct consequence of MF exposure. The

use of MF is instrumental for the diagnosis and therapy (theranostic) of cancer. MRI is

instrumental for the precise diagnosis of different cancers, whereas MNPs open the new

era of nanomedicine, allowing (i) the smart delivery of anticancer drugs, (ii) nanosurgery

through their magnetomechanic properties, and (iii) ﬁghting the cancer cells in situ by

exploiting their capability to generate heat (hyperthermia) via hysteresis loss or through

Neel- and Brownian relaxation losses.

Figure

summarizes the effects of MFs on cancer that were discussed in this review.

Although humans do not perceive the presence or changes of MFs, variations in MF

intensity and inclination exert biological effects, with the greatest effects observed at the

cellular and subcellular level. The basic response to MF relies on ROS-production with RPM

playing a potential role in magneto-perception. Scientists do not generally agree that there is

a cause-effect relationship between exposure to MF and cancer, also because of the difﬁculty

in obtaining reproducible effects that are consistent with the hypothesis that MF may cause

or promote cancer. MFs might not be the direct cause of cancer but may contribute to

ROS-production and generate oxidative stress through RPM [611], which could trigger

or enhance the expression of oncogenes [612]. Large-scale epidemiological studies are

needed to help resolve these issues along with in depth studies on the relationship between

magnetoreception, ROS-generation, and cancer.

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30 of 54

Figure 7. The effects of MFs on cancer. Humans are exposed to a complex mix of man‐made electric

Figure 7.

The effects of MFs on cancer. Humans are exposed to a complex mix of man-made electric

and magnetic fields at many different frequencies both at home and at work. Epidemiological stud‐

and magnetic ﬁelds at many different frequencies both at home and at work. Epidemiological

ies indicate that there is a positive relationship (solid lines) between residential/domestic and occu‐

studies indicate that there is a positive relationship (solid lines) between residential/domestic and

pational exposure to ELF EMF and brain cancer, although some other studies indicate that there is

occupational exposure to ELF EMF and brain cancer, although some other studies indicate that there

no relationship (dotted lines). Breast cancer appears to be more related to residential/domestic ex‐

posure than occupational and in both epidemiological surveys, the so‐called “melatonin hypothe‐

is no relationship (dotted lines). Breast cancer appears to be more related to residential/domestic

sis” finds weak evidence. Testicular/prostatic cancer is associated with residential/domestic expo‐

exposure than occupational and in both epidemiological surveys, the so-called “melatonin hypothesis”

sure, as is leukemia, which is mostly associated (particularly in children) with the close proximity

ﬁnds weak evidence. Testicular/prostatic cancer is associated with residential/domestic exposure,

to ELF EMF. The cellular and in vitro studies on both animal (mainly rat and mice) and human cells

as is leukemia, which is mostly associated (particularly in children) with the close proximity to

indicate the role of ROS‐generation as a consequence of exposure to different MF intensity and tim‐

ELF EMF. The cellular and in vitro studies on both animal (mainly rat and mice) and human cells

ing, suggesting also a magnetoreception mechanism that is based on RPs. Finally, MFs can be used

indicate the role of ROS-generation as a consequence of exposure to different MF intensity and timing,

for theranostic applications; MRI is instrumental for the diagnosis of several cancers, whereas the

suggesting also a magnetoreception mechanism that is based on RPs. Finally, MFs can be used

use of MNP allows the treatment of cancer by nanomedical applications for the precise delivery of

for theranostic applications; MRI is

magnetomechanic methods, and the selective killing of cancer

anticancer drugs, nanosurgery by

instrumental for the diagnosis of several cancers, whereas the

use of MNP allows the treatment of cancer by nanomedical applications for the precise delivery of

cells by magnetic hyperthermia.

anticancer drugs, nanosurgery by magnetomechanic methods, and the selective killing of cancer cells

Supplementary Materials:

by magnetic hyperthermia.

The following supporting information can be downloaded at:

www.mdpi.com/xxx/s1.

Funding: This work was supported by the University of Turin local research funds to M.E.M.

Supplementary Materials:

The following supporting information can be downloaded at:

https:

Data Availability Statement: Supporting Data File S1 contains the EndNote Library used for this

//www.mdpi.com/article/10.3390/ijms23031339/s1.

review.

Funding:

This work was supported by the University of Turin local research funds to M.E.M.

Conflicts of Interest: The author declares no conflict of interest.

Data Availability Statement:

Supporting Data File S1 contains the EndNote Library used for this review.

Conﬂicts of Interest:

The author declares no conﬂict of interest.

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Abbreviations

[F-18]-FDG

ADC

ALL

AMF

AMI

AML

B-ALL

C-11-MET

CAD

CAT

CCHD

CLL

CML

CYP-450

DAB

DCE

DGE

D-MRI

Dox

DSC-MRI

DVBT

DWI

EHS

ELF

EMF

EMVI

ERK1/2

FMT

GdEOB-DTPA

GfM

GIS

GMF

GPX

HR MRI

HVDC

HVOTL

IARC

ICNIRP

IDH

IRPA

LF EMF

MCE-MRI

MDA

MDCT

MHT

MION

MNP

Desoxy Glucose

Apparent Diffusion Coefﬁcient

Alternating Field

Acute Leukemia

Acute Lymphoblastic/lymphocytic Leukemia

Amplitude Modulation

Alternating Magnetic Field

Acute Myocardial Infarction

Acute Myeloid Leukemia

B-Lineage Acute Lymphoblastic Leukemia

C–methyl-L-Methionine

Computer-Aided Diagnosis

Catalase

Chronic Coronary Heart Disease

Chronic Lymphocytic Leukemia

Chronic Myeloid Leukemia

Computed Tomography

Cytochrome P450

Digital Audio Broadcasting

Dynamic Contrast-Enhanced

Dynamic Glucose-Enhanced

Diffusion Magnetic Resonance Imaging

Doxorubicin

Dynamic Susceptibility Contrast Magnetic Resonance Imaging

Digital Video Broadcasting Terrestrial

Diffusion-Weighted Imaging

Electromagnetic Hypersensitivity

Extremely Low Frequency

Electromagnetic

Electromagnetic Field

Extramural Venous Invasion

Extracellular Signal-Regulated Kinases 1/2

Frequency Modulation

Fluorescence Molecular Tomography

Gadolinium-Ethoxybenzyl-Diethylenetriamine Pentaacetic Acid

Gadoﬂuorine-M

Geographical Information System

Geomagnetic Field

Glutathione Peroxidase

High-Resolution Magnetic Resonance Imaging

High Voltage Direct Current

High-Voltage Overhead Transmission Line

International Agency for Research on Cancer

International Commission on Non-Ionizing Radiation Protection

Isocitrate Dehydrogenase

International Radiation Protection Association

Low-Frequency

Low Frequency Electromagnetic Field

Lymphatic Leukemia

Multiphase Contrast-Enhanced Magnetic Resonance Imaging

Malondialdehyde

Multi Detector Computed Tomography

Magnetic Field

Magnetic Hyperthermia Treatment

Magnetic Iron Oxide Nanoparticles

Magnetic NanoParticle

Fluor

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Mp-MRI

MRI

Na-23-MRI

NI EMF

NMMA

NMR

NPP

PET

PFF

PKC

RFH

RFID

RNS

ROS

RPM

SAR

SMF

SOD

SPIO

SPION

TAR

TSMI

TVR

ULF

USPIO

WB-MRI

WHO

WLAN

Multi-Parametric Magnetic Resonance Imaging

Magnetic Resonance Imaging

Na-23-Magnetic Resonance Imaging

Non Ionizing

Non-Ionizing Electromagnetic Field

Nano-Magnetomechanical Activation

Nuclear Magnetic Resonance

Nuclear Power Plants

Odd Ratio

Positron Emission Tomography

Power-Frequency Field

Protein Kinases C

Radio Frequency

Radiofrequency Heat

Radio Frequency Range

Reactive Nitrogen Species

Reactive Oxygen Species

Radical Pair

Radical Pair Mechanism

Singlet State

Speciﬁc Absorption Rate

Static Field

Static Magnetic Field

Superoxide Dismutase

Superparamagnetic Iron Oxide

Superparamagnetic Iron Oxide Nanoparticle

Triplet State

Tumor Area Ratio

Tumor-Selective Molecular Imaging

Tumor Volume Ratio

Ultra-Low Frequency

Ultrasmall Superparamagnetic Particles of Iron Oxide

Whole Body Magnetic Resonance Imaging

World Health Organization

Wireless Local Area Networks

References

Monadizadeh, S.; Kibert, C.J.; Li, J.X.; Woo, J.; Asutosh, A.; Roostaie, S.; Kouhirostami, M. A review of protocols and guidelines

addressing the exposure of occupants to electromagnetic ﬁeld radiation (EMFr) in buildings.

J. Green Build.

2021,

16,

55–81.

[CrossRef]

Rathebe, P.C.; Modisane, D.S.; Rampedi, M.B.; Biddesay-Manilal, S.; Mbonane, T.P. A review on residential exposure to

electromagnetic ﬁelds from overhead power lines: Electriﬁcation as a health burden in rural communities. In Proceedings of the

2019 Open Innovations (OI), Cape Town, South Africa, 2–4 October 2019; pp. 219–221.

Gupta, S.; Sharma, R.S.; Singh, R. Non-ionizing radiation as possible carcinogen.

Int. J. Environ. Health Res.

2020,

1–25. [CrossRef]

[PubMed]

Mazzanti, G. Evaluation of continuous exposure to magnetic ﬁeld from ac overhead transmission lines via historical load

databases: Common procedures and innovative heuristic formulas.

IEEE Trans. Power Deliv.

2010,

25,

238–247. [CrossRef]

Landler, L.; Keays, D.A. Cryptochrome: The magnetosensor with a sinister side?

PLoS Biol.

2018,

16,

e3000018. [CrossRef]

[PubMed]

Jacobson, J.I. Speculations on the inﬂuence of electromagnetism on genomic and associated structures.

J. Int. Med. Res.

1996,

24,

1–11. [CrossRef]

Repacholi, M. Concern that “EMF” magnetic ﬁelds from power lines cause cancer.

Sci. Total Environ.

2012,

426,

454–458. [CrossRef]

[PubMed]

Saliev, T.; Begimbetova, D.; Masoud, A.R.; Matkarimov, B. Biological effects of non-ionizing electromagnetic ﬁelds: Two sides of a

coin.

Prog. Biophys. Mol. Biol.

2019,

141,

25–36. [CrossRef] [PubMed]

Lednev, V.V. Possible mechanism for the inﬂuence of weak magnetic ﬁelds on biological systems.

Bioelectromagnetics

1991,

12,

71–75. [CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

32 of 55

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

Occhipinti, A.; De Santis, A.; Maffei, M.E. Magnetoreception: An unavoidable step for plant evolution?

Trends Plant Sci

2014,

19,

1–4. [CrossRef] [PubMed]

Maffei, M.E. Magnetic ﬁeld effects on plant growth, development, and evolution.

Front. Plant Sci.

2014,

445. [CrossRef]

Swanson, J.; Kheifets, L. Could the geomagnetic ﬁeld be an effect modiﬁer for studies of power-frequency magnetic ﬁelds and

childhood leukaemia?

J. Radiol. Prot.

2012,

32,

413–418. [CrossRef] [PubMed]

Amoon, A.T.; Swanson, J.; Vergara, X.; Kheifets, L. Relationship between distance to overhead power lines and calculated ﬁelds in

two studies.

J. Radiol. Prot.

2020,

40,

431–443. [CrossRef] [PubMed]

Wyszkowska, J.; Szczygiel, M.; Trawinski, T. Static magnetic ﬁeld and extremely low-frequency magnetic ﬁeld in hybrid and

electric vehicles.

Prz. Elektrotech.

2020,

96,

60–62. [CrossRef]

Vaitl, D.; Propson, N.; Stark, R.; Schienle, A. Natural very-low-frequency sferics and headache.

Int. J. Biometeorol.

2001,

45,

115–123. [CrossRef] [PubMed]

Feychting, M.; Ahlborn, A.; Kheifets, L. EMF and health.

Annu. Rev. Public Health

2005,

26,

165–189. [CrossRef] [PubMed]

Repacholi, M.H. Who’s international EMF project.

Radiat. Prot. Dosim.

1999,

83,

1–4. [CrossRef]

Bowman, J.D.; Ray, T.K.; Park, R.M. Possible health beneﬁts from reducing occupational magnetic ﬁelds.

Am. J. Ind. Med.

2013,

56,

791–805. [CrossRef] [PubMed]

Belyaev, I.; Dean, A.; Eger, H.; Hubmann, G.; Jandrisovits, R.; Kern, M.; Kundi, M.; Moshammer, H.; Lercher, P.; Muller, K.; et al.

Europaem EMF guideline 2016 for the prevention, diagnosis and treatment of EMF-related health problems and illnesses.

Rev.

Environ. Health

2016,

31,

363–397. [CrossRef]

Repacholi, M.H. A history of the international commission on non-ionizing radiation protection.

Health Phys.

2017,

113,

282–300.

[CrossRef] [PubMed]

Soto Sumuano, J.L.; Abundis Gutierrez, E.; Tlacuilo-Parra, J.A.; Garibaldi Covarrubias, R.F.; Romo Rubio, H. Electromagnetic

radiation, childhood leukemia and regulation.

Rev. Int. Contam. Ambient.

2020,

36,

229–240.

Knave, B. Electromagnetic ﬁelds and health outcomes.

Ann. Acad. Med. Singap.

2001,

30,

489–493. [PubMed]

Kelfkens, G.; Pruppers, M. Magnetic ﬁelds and childhood leukemia; science and policy in the Netherlands. In Proceedings of

the Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference

on Biomedical Engineering and Medical Physics (NBC), Tampere, Finland, 11–15 June 2017; Eskola, H., Vaisanen, O., Viik, J.,

Hyttinen, J., Eds.; Springer: Singapore, 2018; Volume 65, pp. 498–501.

Teepen, J.C.; van Dijck, J. Impact of high electromagnetic ﬁeld levels on childhood leukemia incidence.

Int. J. Cancer

2012,

131,

769–778. [CrossRef] [PubMed]

Calvente, I.; Fernandez, M.F.; Villalba, J.; Olea, N.; Nunez, M.I. Exposure to electromagnetic ﬁelds (non-ionizing radiation) and its

relationship with childhood leukemia: A systematic review.

Sci. Total Environ.

2010,

408,

3062–3069. [CrossRef] [PubMed]

Calvente, I.; Davila-Arias, C.; Ocon-Hernandez, O.; Perez-Lobato, R.; Ramos, R.; Artacho-Cordon, F.; Olea, N.; Nunez, M.I.;

Fernandez, M.F. Characterization of indoor extremely low frequency and low frequency electromagnetic ﬁelds in the inma-

granada cohort.

PLoS ONE

2014,

e106666. [CrossRef]

Miah, T.; Kamat, D. Current understanding of the health effects of electromagnetic ﬁelds.

Pediatric Ann.

2017,

46,

E172–E174.

[CrossRef] [PubMed]

Bates, M.N. Extremely low-frequency electromagnetic-ﬁelds and cancer—The epidemiologic evidence.

Environ. Health Perspect.

1991,

95,

147–156. [CrossRef]

Wertheimer, N.; Leeper, E. Electrical wiring conﬁgurations and childhood cancer.

Am. J. Epidemiol.

1979,

109,

273–284. [CrossRef]

[PubMed]

IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Non-ionizing radiation, part 2: Radiofrequency

electromagnetic ﬁelds.

IARC Monogr. Eval. Carcinog. Risks Hum.

2013,

102 Pt 2,

1–460.

Deutsch, S.; Wilkening, G.M. Electromagnetic ﬁeld cancer scares.

Health Phys.

1997,

73,

301–309. [CrossRef] [PubMed]

Karimi, A.; Moghaddam, F.G.; Valipour, M. Insights in the biology of extremely low-frequency magnetic ﬁelds exposure on

human health.

Mol. Biol. Rep.

2020,

47,

5621–5633. [CrossRef] [PubMed]

Moulder, J.E.; Foster, K.R. Biological effects of power-frequency ﬁelds as they relate to carcinogenesis.

Proc. Soc. Exp. Biol. Med.

1995,

209,

309–324. [CrossRef] [PubMed]

Dieudonne, M. Electromagnetic hypersensitivity: A critical review of explanatory hypotheses.

Environ. Health

2020,

19,

48.

[CrossRef] [PubMed]

Elliott, P.; Shaddick, G.; Douglass, M.; de Hoogh, K.; Briggs, D.J.; Toledano, M.B. Adult cancers near high-voltage overhead power

lines.

Epidemiology

2013,

24,

184–190. [CrossRef]

Lambrozo, J. Electric and magnetic ﬁelds with a frequency of 50–60 hz: Assessment of 20 years of research.

Indoor Built Environ.

2001,

10,

299–305.

Souques, M.; Lambrozo, J. 50–60 hz magnetic ﬁelds and health: What’s new?

Radioprotection

2015,

50,

95–99. [CrossRef]

Sheppard, A.R.; Kavet, R.; Renew, D.C. Exposure guidelines for low-frequency electric and magnetic ﬁelds: Report from the

brussels workshop.

Health Phys.

2002,

83,

324–332. [CrossRef] [PubMed]

Maes, A.; Verschaeve, L. Genetic damage in humans exposed to extremely low-frequency electromagnetic ﬁelds.

Arch. Toxicol.

2016,

90,

2337–2348. [CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

33 of 55

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62.

63.

64.

65.

66.

Lacy-Hulbert, A.; Metcalfe, J.C.; Hesketh, R. Biological responses to electromagnetic ﬁelds.

FASEB J.

1998,

12,

395–420. [CrossRef]

[PubMed]

Kavet, R. EMF and current cancer concepts.

Bioelectromagnetics

1996,

17,

339–357. [CrossRef]

Rathebe, P.; Weyers, C.; Raphela, F. Exposure levels of elf magnetic ﬁelds in the residential areas of mangaung metropolitan

municipality.

Environ. Monit. Assess.

2018,

190,

544. [CrossRef] [PubMed]

Navarro-Camba, E.A.; Segura-Garcia, J.; Gomez-Perretta, C. Exposure to 50 hz magnetic ﬁelds in homes and areas surrounding

urban transformer stations in silla (spain): Environmental impact assessment.

Sustainability

2018,

10,

2641. [CrossRef]

Sadeghi, T.; Ahmadi, A.; Javadian, M.; Gholamian, S.A.; Delavar, M.A.; Esmailzadeh, S.; Ahmadi, B.; Hadighi, M.S.H. Preterm

birth among women living within 600 meters of high voltage overhead power lines: A case-control study.

Rom. J. Intern. Med.

2017,

55,

145–150. [CrossRef] [PubMed]

Ahlbom, A.; Cardis, E.; Green, A.; Linet, M.; Savitz, D.; Swerdlow, A.; Epidemiology, I.S.C. Review of the epidemiologic literature

on EMF and health.

Environ. Health Perspect.

2001,

109,

911–933. [PubMed]

Alonso, A.; Bahillo, A.; de la Rosa, R.; Carrera, A.; Duran, R.J.; Fernandez, P. Measurement procedure to assess exposure to

extremely low-frequency ﬁelds: A primary school case study.

Radiat. Prot. Dosim.

2012,

151,

426–436. [CrossRef] [PubMed]

Li, C.Y.; Sung, F.C.; Chen, F.L.; Lee, P.C.; Silva, M.; Mezei, G. Extremely-low-frequency magnetic ﬁeld exposure of children at

schools near high voltage transmission lines.

Sci. Total Environ.

2007,

376,

151–159. [CrossRef] [PubMed]

Tognola, G.; Chiaramello, E.; Bonato, M.; Magne, I.; Souques, M.; Fiocchi, S.; Parazzini, M.; Ravazzani, P. Cluster analysis of

residential personal exposure to elf magnetic ﬁeld in children: Effect of environmental variables.

Int. J. Environ. Res. Public Health

2019,

16,

4363. [CrossRef] [PubMed]

Huss, A.; Goris, K.; Vermeulen, R.; Kromhout, H. Does apartment’s distance to an in-built transformer room predict magnetic

ﬁeld exposure levels?

J. Expo. Sci. Environ. Epidemiol.

2013,

23,

554–558. [CrossRef] [PubMed]

Roosli, M.; Jenni, D.; Kheifets, L.; Mezei, G. Extremely low frequency magnetic ﬁeld measurements in buildings with transformer

stations in switzerland.

Sci. Total Environ.

2011,

409,

3364–3369. [CrossRef] [PubMed]

Ilonen, K.; Markkanen, A.; Mezei, G.; Juutilainen, J. Indoor transformer stations as predictors of residential elf magnetic ﬁeld

exposure.

Bioelectromagnetics

2008,

29,

213–218. [CrossRef]

Vergara, X.P.; Kavet, R.; Crespi, C.M.; Hooper, C.; Silva, J.M.; Kheifets, L. Estimating magnetic ﬁelds of homes near transmission

lines in the california power line study.

Environ. Res.

2015,

140,

514–523. [CrossRef]

Aldrich, T.E.; Andrews, K.W.; Liboff, A.R. Brain cancer risk and electromagnetic ﬁelds (EMFs): Assessing the geomagnetic

component.

Arch. Environ. Health

2001,

56,

314–319. [CrossRef]

Nikkila, A.; Kendall, G.; Raitanen, J.; Spycher, B.; Lohi, O.; Auvinen, A. Effects of incomplete residential histories on studies of

environmental exposure with application to childhood leukaemia and background radiation.

Environ. Res.

2018,

166,

466–472.

[CrossRef] [PubMed]

Greenland, S.; Kheifets, L.; Zafanella, L.E.; Kalton, G.W. Leukemia attributable to residential magnetic ﬁelds: Results from

analyses allowing for study biases.

Risk Anal.

2006,

26,

471–482. [CrossRef]

Miller, K.D.; Ostrom, Q.T.; Kruchko, C.; Patil, N.; Tihan, T.; Ciofﬁ, G.; Fuchs, H.E.; Waite, K.A.; Jemal, A.; Siegel, R.L.; et al. Brain

and other central nervous system tumor statistics, 2021.

CA Cancer J. Clin.

2021,

71,

381–406. [CrossRef]

Farmanfarma, K.K.; Mohammadian, M.; Shahabinia, Z.; Hassanipour, S.; Salehiniya, H. Brain cancer in the world: An epidemio-

logical review.

World Cancer Res. J.

2019,

Pourreza, R.; Zhuge, Y.; Ning, H.; Miller, R. Brain tumor segmentation in MRI scans using deeply-supervised neural networks. In

Proceedings of the 3rd International Workshop on Brain-Lesion (BrainLes) Held Jointly at the Conference on Medical Image

Computing for Computer Assisted Intervention (MICCAI), Quebec City, QC, Canada, 14 September 2017; Springer International

Publishing Ag: Quebec City, QC, Canada, 2018; pp. 320–331.

Kheifets, L.I. Electric and magnetic ﬁeld exposure and brain cancer: A review.

Bioelectromagnetics

2001,

22,

S120–S131. [CrossRef]

Schoenfeld, E.R.; Henderson, K.; O’Leary, E.; Grimson, R.; Kaune, W.; Leske, M.C. Magnetic ﬁeld exposure assessment: A

comparison of various methods.

Bioelectromagnetics

1999,

20,

487–496. [CrossRef]

Eskelinen, T.; Keinänen, J.; Salonen, H.; Juutilainen, J. Use of spot measurements for assessing residential elf magnetic ﬁeld

exposure: A validity study.

Bioelectromagnetics

2002,

23,

173–176. [CrossRef] [PubMed]

Ahmadi, H.; Mohseni, S.; Akmal, A.A.S. Electromagnetic ﬁelds near transmission lines—Problems and solutions.

Iran. J. Environ.

Health Sci. Eng.

2010,

181–188.

Kheifets, L.; Crespi, C.M.; Hooper, C.; Oksuzyan, S.; Cockburn, M.; Ly, T.; Mezei, G. Epidemiologic study of residential proximity

to transmission lines and childhood cancer in california: Description of design, epidemiologic methods and study population.

Expo. Sci. Environ. Epidemiol.

2015,

25,

45–52. [CrossRef] [PubMed]

Blaasaas, K.G.; Tynes, T. Comparison of three different ways of measuring distances between residences and high voltage power

lines.

Bioelectromagnetics

2002,

23,

288–291. [CrossRef] [PubMed]

Carles, C.; Esquirol, Y.; Turuban, M.; Piel, C.; Migault, L.; Pouchieu, C.; Bouvier, G.; Fabbro-Peray, P.; Lebailly, P.; Baldi, I.

Residential proximity to power lines and risk of brain tumor in the general population.

Environ. Res.

2020,

185,

109473. [CrossRef]

Marcilio, I.; Gouveia, N.; Pereira Filho, M.L.; Kheifets, L. Adult mortality from leukemia, brain cancer, amyotrophic lateral

sclerosis and magnetic ﬁelds from power lines: A case-control study in Brazil.

Rev. Bras. Epidemiol.

2011,

14,

580–588. [CrossRef]

[PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

34 of 55

67.

68.

69.

70.

71.

72.

73.

74.

75.

76.

77.

78.

79.

80.

81.

82.

83.

84.

85.

86.

87.

88.

89.

90.

91.

Baldi, I.; Coureau, G.; Jaffré, A.; Gruber, A.; Ducamp, S.; Provost, D.; Lebailly, P.; Vital, A.; Loiseau, H.; Salamon, R. Occupational

and residential exposure to electromagnetic ﬁelds and risk of brain tumors in adults: A case–control study in Gironde, France.

Int.

J. Cancer

2011,

129,

1477–1484. [CrossRef] [PubMed]

Li, C.Y.; Lin, R.S.; Sung, F.C. Elevated residential exposure to power frequency magnetic ﬁeld associated with greater average age

at diagnosis for patients with brain tumors.

Bioelectromagnetics

2003,

24,

218–221. [CrossRef] [PubMed]

Carpenter, D.O. Extremely low frequency electromagnetic ﬁelds and cancer: How source of funding affects results.

Environ. Res

2019,

178,

108688. [CrossRef] [PubMed]

Mezei, G.; Gadallah, M.; Kheifets, L. Residential magnetic ﬁeld exposure and childhood brain cancer—A meta-analysis.

Epidemi-

ology

2008,

19,

424–430. [CrossRef] [PubMed]

Kheifets, L.; Ahlbom, A.; Crespi, C.M.; Feychting, M.; Johansen, C.; Monroe, J.; Murphy, M.F.G.; Oksuzyan, S.; Preston-Martin, S.;

Roman, E.; et al. A pooled analysis of extremely low-frequency magnetic ﬁelds and childhood brain tumors.

Am. J. Epidemiol.

2010,

172,

752–761. [CrossRef] [PubMed]

Saito, T.; Nitta, H.; Kubo, O.; Yamamoto, S.; Yamaguchi, N.; Akiba, S.; Honda, Y.; Hagihara, J.; Isaka, K.; Ojima, T.; et al.

Power-frequency magnetic ﬁelds and childhood brain tumors: A case-control study in Japan.

J. Epidemiol.

2010,

20,

54–61.

[CrossRef] [PubMed]

Kroll, M.E.; Swanson, J.; Vincent, T.J.; Draper, G.J. Childhood cancer and magnetic ﬁelds from high-voltage power lines in england

and wales: A case-control study.

Br. J. Cancer

2010,

103,

1122–1127. [CrossRef]

Seomun, G.; Lee, J.; Park, J. Exposure to extremely low-frequency magnetic ﬁelds and childhood cancer: A systematic review and

meta-analysis.

PLoS ONE

2021,

16,

e0251628. [CrossRef]

Ozen, S. Low-frequency transient electric and magnetic ﬁelds coupling to child body.

Radiat. Prot. Dosim.

2008,

128,

62–67.

[CrossRef]

Miller, K.D.; Nogueira, L.; Mariotto, A.B.; Rowland, J.H.; Yabroff, K.R.; Alfano, C.M.; Jemal, A.; Kramer, J.L.; Siegel, R.L. Cancer

treatment and survivorship statistics, 2019.

CA Cancer J. Clin.

2019,

69,

363–385. [CrossRef]

Davis, S.; Mirick, D.K.; Chen, C.; Stanczyk, F.Z. Effects of 60-hz magnetic ﬁeld exposure on nocturnal 6-sulfatoxymelatonin,

estrogens, luteinizing hormone, and follicle, stimulating hormone in healthy reproductive-age women: Results of a crossover

trial.

Ann. Epidemiol.

2006,

16,

622–631. [CrossRef] [PubMed]

Graham, C.; Cook, M.R.; Gerkovich, M.M.; Sastre, A. Melatonin and 6-ohms in high-intensity magnetic ﬁelds.

J. Pineal Res.

2001,

31,

85–88. [CrossRef] [PubMed]

Youngstedt, S.D.; Kripke, D.F.; Elliott, J.A.; Assmus, J.D. No association of 6-sulfatoxymelatonin with in-bed 60-hz magnetic ﬁeld

exposure or illumination level among older adults.

Environ. Res.

2002,

89,

201–209. [CrossRef]

Levallois, P.; Dumont, M.; Touitou, Y.; Gingras, S.; Masse, B.; Gauvin, D.; Kroger, E.; Bourdages, M.; Douville, P. Effects of electric

and magnetic ﬁelds from high-power lines on female urinary excretion of 6-sulfatoxymelatonin.

Am. J. Epidemiol.

2001,

154,

601–609. [CrossRef] [PubMed]

Feychting, M. Invited commentary: Extremely low-frequency magnetic ﬁelds and breast cancerunow it is enough!

Am. J.

Epidemiol.

2013,

178,

1046–1050. [CrossRef] [PubMed]

Touitou, Y.; Lambrozo, J.; Camus, F.O.; Charbuy, H. Magnetic ﬁelds and the melatonin hypothesis: A study of workers chronically

exposed to 50-hz magnetic ﬁelds.

Am. J. Physiol. Regul. Integr. Comp. Physiol.

2003,

284,

R1529–R1535. [CrossRef] [PubMed]

Touitou, Y.; Selmaoui, B.; Lambrozo, J.; Auzeby, A. Assessment of the effects of magnetic ﬁelds (50 hz) on melatonin secretion in

humans and rats. A circadian study.

Bull. Acad. Natl. Med.

2002,

186,

1625–1639. [PubMed]

Davis, S.; Mirick, D.K. Residential magnetic ﬁelds, medication use, and the risk of breast cancer.

Epidemiology

2007,

18,

266–269.

[CrossRef] [PubMed]

de Bruyn, L.; de Jager, L.; Kuyl, J.M. The inﬂuence of long-term exposure of mice to randomly varied power frequency magnetic

ﬁelds on their nocturnal melatonin secretion patterns.

Environ. Res.

2001,

85,

115–121. [CrossRef] [PubMed]

O’Leary, E.S.; Schoenfeld, E.R.; Henderson, K.; Grimson, R.; Kabat, G.C.; Kaune, W.T.; Gammon, M.D.; Leske, C.; Grp, E. Wire

coding in the EMF and breast cancer on long island study: Relationship to magnetic ﬁelds.

J. Expo. Anal. Environ. Epidemiol.

2003,

13,

283–293. [CrossRef]

London, S.J.; Pogoda, J.M.; Hwang, K.L.; Langholz, B.; Monroe, K.R.; Kolonel, L.N.; Kaune, W.T.; Peters, J.M.; Henderson, B.E.

Residential magnetic ﬁeld exposure and breast cancer risk: A nested case-control study from a multiethnic cohort in los angeles

county, california.

Am. J. Epidemiol.

2003,

158,

969–980. [CrossRef] [PubMed]

Haugsdal, B.; Tynes, T.; Rotnes, J.S.; Grifﬁths, D. A single nocturnal exposure to 2–7 millitesla static magnetic ﬁelds does not

inhibit the excretion of 6-sulfatoxymelatonin in healthy young men.

Bioelectromagnetics

2001,

22,

1–6. [CrossRef]

Fedrowitz, M.; Westermann, J.; Loscher, W. Magnetic ﬁeld exposure increases cell proliferation but does not affect melatonin

levels in the mammary gland of female sprague dawley rats.

Cancer Res.

2002,

62,

1356–1363. [PubMed]

Vanderstraeten, J.; Verschaeve, L.; Burda, H.; Bouland, C.; Brouwer, C. Health effects of extremely low-frequency magnetic ﬁelds:

Reconsidering the melatonin hypothesis in the light of current data on magnetoreception.

J. Appl. Toxicol.

2012,

32,

952–958.

[CrossRef]

Zhang, Y.M.; Lai, J.S.; Ruan, G.R.; Chen, C.; Wang, D.W. Meta-analysis of extremely low frequency electromagnetic ﬁelds and

cancer risk: A pooled analysis of epidemiologic studies.

Environ. Int.

2016,

88,

36–43. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

35 of 55

92.

93.

94.

95.

96.

97.

98.

99.

100.

101.

102.

103.

104.

105.

106.

107.

108.

109.

110.

111.

112.

113.

114.

115.

116.

Zhu, K.M.; Hunter, S.; Payne-Wilks, K.; Roland, C.L.; Forbes, D.S. Use of electric bedding devices and risk of breast cancer in

african-american women.

Am. J. Epidemiol.

2003,

158,

798–806. [CrossRef] [PubMed]

Laden, F.; Hunter, D.J. Environmental risk factors and female breast cancer.

Annu. Rev. Public Health

1998,

19,

101–123. [CrossRef]

[PubMed]

Amoon, A.T.; Swanson, J.; Magnani, C.; Johansen, C.; Kheifets, L. Pooled analysis of recent studies of magnetic ﬁelds and

childhood leukemia.

Environ. Res.

2022,

204,

7. [CrossRef] [PubMed]

Elwood, J.M. Childhood leukemia and residential magnetic ﬁelds: Are pooled analyses more valid than the original studies?

Bioelectromagnetics

2006,

27,

112–118. [CrossRef] [PubMed]

Schuz, J. Exposure to extremely low-frequency magnetic ﬁelds and the risk of childhood cancer: Update of the epidemiological

evidence.

Prog. Biophys. Mol. Biol.

2011,

107,

339–342. [CrossRef]

Ghahremani, S.; Shiroudbakhshi, K.; Kordasiabi, A.H.S.; FiroozBakht, M.; Hosseinzadegan, M.; Ashraﬁnia, F.; Rahafard, S.

Exposure to magnetic ﬁelds and childhood leukemia: An overview of meta-analysis.

Int. J. Pediatr.-Mashhad

2020,

11361–11365.

Tafrishi, R.; Seyfari, B.; Rahimi, R.; Chaichi, Z.; Tarazjani, A.D.; Marvi, N.; Maazallahi, M.; Dolatian, Z.; Ashraﬁnia, F. Modiﬁable

and non-modiﬁable factors affecting the risk of childhood leukemia: An overview of meta-analysis.

Int. J. Pediatr.

2021,

13243–13248.

Zhao, L.Y.; Liu, X.D.; Wang, C.P.; Yan, K.K.; Lin, X.J.; Li, S.; Bao, H.H.; Liu, X. Magnetic ﬁelds exposure and childhood leukemia

risk: A meta-analysis based on 11,699 cases and 13,194 controls.

Leuk. Res.

2014,

38,

269–274. [CrossRef] [PubMed]

Amoon, A.T.; Crespi, C.M.; Ahlbom, A.; Bhatnagar, M.; Bray, I.; Bunch, K.J.; Clavel, J.; Feychting, M.; Hemon, D.; Johansen, C.;

et al. Proximity to overhead power lines and childhood leukaemia: An international pooled analysis.

Br. J. Cancer

2018,

119,

364–373. [CrossRef] [PubMed]

Pedersen, C.; Brauner, E.V.; Rod, N.H.; Albieri, V.; Andersen, C.E.; Ulbak, K.; Hertel, O.; Johansen, C.; Schuz, J.; Raaschou-Nielsen,

O. Distance to high-voltage power lines and risk of childhood leukemia—An analysis of confounding by and interaction with

other potential risk factors.

PLoS ONE

2014,

e107096. [CrossRef] [PubMed]

Pedersen, C.; Johansen, C.; Schuz, J.; Olsen, J.H.; Raaschou-Nielsen, O. Residential exposure to extremely low-frequency magnetic

ﬁelds and risk of childhood leukaemia, cns tumour and lymphoma in denmark.

Br. J. Cancer

2015,

113,

1370–1374. [CrossRef]

Nunez-Enriquez, J.C.; Correa-Correa, V.; Flores-Lujano, J.; Perez-Saldivar, M.L.; Jimenez-Hernandez, E.; Martin-Trejo, J.A.;

Espinoza-Hernandez, L.E.; Medina-Sanson, A.; Cardenas-Cardos, R.; Flores-Villegas, L.V.; et al. Extremely low-frequency

magnetic ﬁelds and the risk of childhood b-lineage acute lymphoblastic leukemia in a city with high incidence of leukemia and

elevated exposure to elf magnetic ﬁelds.

Bioelectromagnetics

2020,

41,

581–597. [CrossRef] [PubMed]

Schuz, J.; Grigat, J.P.; Brinkmann, K.; Michaelis, J. Residential magnetic ﬁelds as a risk factor for childhood acute leukaemia:

Results from a german population-based case-control study.

Int. J. Cancer

2001,

91,

728–735. [CrossRef]

Kheifets, L.; Crespi, C.M.; Hooper, C.; Cockburn, M.; Amoon, A.T.; Vergara, X.P. Residential magnetic ﬁelds exposure and

childhood leukemia: A population-based case-control study in california.

Cancer Causes Control

2017,

28,

1117–1123. [CrossRef]

Crespi, C.M.; Swanson, J.; Vergara, X.P.; Kheifets, L. Childhood leukemia risk in the california power line study: Magnetic ﬁelds

versus distance from power lines.

Environ. Res.

2019,

171,

530–535. [CrossRef] [PubMed]

Crespi, C.M.; Vergara, X.P.; Hooper, C.; Oksuzyan, S.; Wu, S.; Cockburn, M.; Kheifets, L. Childhood leukaemia and distance from

power lines in california: A population-based case-control study.

Br. J. Cancer

2016,

115,

122–128. [CrossRef]

Sermage-Faure, C.; Demoury, C.; Rudant, J.; Goujon-Bellec, S.; Guyot-Goubin, A.; Deschamps, F.; Hemon, D.; Clavel, J. Childhood

leukaemia close to high-voltage power lines—The geocap study, 2002–2007.

Br. J. Cancer

2013,

108,

1899–1906. [CrossRef]

Tabrizi, M.M.; Bidgoli, S.-A. Increased risk of childhood acute lymphoblastic leukemia (all) by prenatal and postnatal exposure to

high voltage power lines: A case control study in isfahan, iran.

Asian Pac. J. Cancer Prev.

2015,

16,

2347–2350. [CrossRef] [PubMed]

Schuz, J.; Dasenbrock, C.; Ravazzani, P.; Roosli, M.; Schar, P.; Bounds, P.L.; Erdmann, F.; Borkhardt, A.; Cobaleda, C.; Fedrowitz, M.;

et al. Extremely low-frequency magnetic ﬁelds and risk of childhood leukemia: A risk assessment by the arimmora consortium.

Bioelectromagnetics

2016,

37,

183–189. [CrossRef] [PubMed]

Sermage-Faure, C.; Laurier, D.; Goujon-Bellec, S.; Chartier, M.; Guyot-Goubin, A.; Rudant, J.; Hemon, D.; Clavel, J. Childhood

leukemia around french nuclear power plantsuthe geocap study, 2002–2007.

Int. J. Cancer

2012,

131,

E769–E780. [CrossRef]

[PubMed]

Ziegelberger, G.; Dehos, A.; Grosche, B.; Hornhardt, S.; Jung, T.; Weiss, W. Childhood leukemia—Risk factors and the need for an

interdisciplinary research agenda.

Prog. Biophys. Mol. Biol.

2011,

107,

312–314. [CrossRef]

Wünsch, V.; Pelissari, D.M.; Barbieri, F.E.; Sant’Anna, L.; de Oliveira, C.T.; de Mata, J.F.; Tone, L.G.; Lee, M.L.D.; de Andrea,

M.L.M.; Bruniera, P.; et al. Exposure to magnetic ﬁelds and childhood acute lymphocytic leukemia in Sao Paulo, Brazil.

Cancer

Epidemiol.

2011,

35,

534–539. [CrossRef]

Jirik, V.; Pekarek, L.; Janout, V.; Tomaskova, H. Association between childhood leukaemia and exposure to power-frequency

magnetic ﬁelds in Middle Europe.

Biomed. Environ. Sci.

2012,

25,

597–601. [PubMed]

Hakim, A.S.B.; Abd Rahman, N.B.; Mokhtar, M.Z.; Bin Said, I.; Hussain, H. ELF-EMF correlation study on distance from overhead

transmission lines and acute leukemia among children in Klang Valley, Malaysia. In Proceedings of the 2014 IEEE Conference on

Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia, 8–10 December 2014; pp. 710–714.

Karipidis, K.K. Survey of residential power-frequency magnetic ﬁelds in Melbourne, Australia.

Radiat. Prot. Dosim.

2015,

163,

81–91. [CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

36 of 55

117. Jirik, V.; Pekarek, L.; Janout, V. Assessment of population exposure to extremely low frequency magnetic ﬁelds and its possible

childhood health risk in the Czech Republic.

Indoor Built Environ.

2011,

20,

362–368. [CrossRef]

118. Kokate, P.A.; Mishra, A.K.; Lokhande, S.K.; Bodhe, G.L. Extremely low frequency electromagnetic ﬁeld (ELF-EMF) and childhood

leukemia (cl) near transmission lines: A review.

Adv. Electromagn.

2016,

30–40. [CrossRef]

119. Zaki, A.M.; Abd Rahim, M.A.; Zaidun, Z.; Ramdzan, A.R.; Isa, Z.M. Exposure to non-ionizing radiation and childhood cancer: A

meta-analysis.

Middle East J. Cancer

2020,

11,

1–11.

120. Swanson, J.; Bunch, K.J. Reanalysis of risks of childhood leukaemia with distance from overhead power lines in the UK.

J. Radiol.

Prot.

2018,

38,

N30–N35. [CrossRef] [PubMed]

121. Liorni, I.; Parazzini, M.; Struchen, B.; Fiocchi, S.; Roosli, M.; Ravazzani, P. Children’s personal exposure measurements to

extremely low frequency magnetic ﬁelds in Italy.

Int. J. Environ. Res. Public Health

2016,

13,

549. [CrossRef]

122. Amoon, A.T.; Arah, O.A.; Kheifets, L. The sensitivity of reported effects of EMF on childhood leukemia to uncontrolled

confounding by residential mobility: A hybrid simulation study and an empirical analysis using caps data.

Cancer Causes Control

2019,

30,

901–908. [CrossRef] [PubMed]

123. Amoon, A.T.; Oksuzyan, S.; Crespi, C.M.; Arah, O.A.; Cockburn, M.; Vergara, X.; Kheifets, L. Residential mobility and childhood

leukemia.

Environ. Res.

2018,

164,

459–466. [CrossRef] [PubMed]

124. Bunch, K.J.; Swanson, J.; Vincent, T.J.; Murphy, M.F.G. Magnetic ﬁelds and childhood cancer: An epidemiological investigation of

the effects of high-voltage underground cables.

J. Radiol. Prot.

2015,

35,

695–705. [CrossRef] [PubMed]

125. Auger, N.; Bilodeau-Bertrand, M.; Marcoux, S.; Kosatsky, T. Residential exposure to electromagnetic ﬁelds during pregnancy and

risk of child cancer: A longitudinal cohort study.

Environ. Res.

2019,

176,

108524. [CrossRef] [PubMed]

126. Soderberg, K.C.; Naumburg, E.; Anger, G.; Cnattingius, S.; Ekbom, A.; Feychting, M. Childhood leukemia and magnetic ﬁelds in

infant incubators.

Epidemiology

2002,

13,

45–49. [CrossRef] [PubMed]

127. Slusky, D.A.; Does, M.; Metayer, C.; Mezei, G.; Selvin, S.; Bufﬂer, P.A. Potential role of selection bias in the association between

childhood leukemia and residential magnetic ﬁelds exposure: A population-based assessment.

Cancer Epidemiol.

2014,

38,

307–313.

[CrossRef] [PubMed]

128. Leitgeb, N. Synoptic analysis clariﬁes childhood leukemia risk from elf magnetic ﬁeld exposure.

J. Electromagn. Anal. Appl.

2015,

10. [CrossRef]

129. Schuz, J.; Erdmann, F. Environmental exposure and risk of childhood leukemia: An overview.

Arch. Med. Res.

2016,

47,

607–614.

[CrossRef]

130. Salvan, A.; Ranucci, A.; Lagorio, S.; Magnani, C.; Grp, S.R. Childhood leukemia and 50 hz magnetic ﬁelds: Findings from the

italian setil case-control study.

Int. J. Environ. Res. Public Health

2015,

12,

2184–2204. [CrossRef]

131. Omura, Y.; Losco, M. Electromagnetic-ﬁelds in the home-environment (color tv, computer monitor, microwave-oven, cellular

phone, etc) as potential contributing factors for the induction of oncogen c-fos ab1, oncogen c-fos ab2, integrin alpha-5-beta-1 and

development of cancer, as well as effects of microwave on amino-acid-composition of food and living human brain.

Acupunct.

Electro-Ther. Res.

1993,

18,

33–73.

132. Verreault, R.; Weiss, N.S.; Hollenbach, K.A.; Strader, C.H.; Daling, J.R. Use of electric blankets and risk of testicular cancer.

Am. J.

Epidemiol.

1990,

131,

759–762. [CrossRef]

133. Zhu, K.; Weiss, N.S.; Stanford, J.L.; Daling, J.R.; Stergachis, A.; McKnight, B.; Brawer, M.K.; Levine, R.S. Prostate cancer in relation

to the use of electric blanket or heated water bed.

Epidemiology

1999,

10,

83–85. [CrossRef]

134. McElroy, J.A.; Newcomb, P.A.; Trentham-Dietz, A.; Hampton, J.M.; Kanarek, M.S.; Remington, P.L. Endometrial cancer incidence

in relation to electric blanket use.

Am. J. Epidemiol.

2002,

156,

262–267. [CrossRef]

135. Gudina, M.V.; Borodin, A.S.; Tuzhilkin, D.A.; Pikalova, L.V. Malignant neoplasms on territories with different levels of magnetic

ﬁelds of industrial frequency. In Proceedings of the 24th International Symposium on Atmospheric and Ocean Optics: Atmo-

spheric Physics, Tomsk, Russia, 2–5 July 2018; Matvienko, G.G., Romanovskii, O.A., Eds.; SPIE—International Society for Optics

and Photonics: Bellingham, WA, USA, 2018; Volume 10833.

136. Hallberg, O.; Johansson, O. Melanoma incidence and frequency modulation (FM) broadcasting.

Arch. Environ. Health

2002,

57,

32–40. [CrossRef]

137. Mahram, M.; Ghazavi, M. The effect of extremely low frequency electromagnetic ﬁelds on pregnancy and fetal growth, and

development.

Arch. Iran. Med.

2013,

16,

221–224.

138. Deadman, J.E.; Infante-Rivard, C. Individual estimation of exposures to extremely low frequency magnetic ﬁelds in jobs commonly

held by women.

Am. J. Epidemiol.

2002,

155,

368–378. [CrossRef]

139. Ptitsyna, N.G.; Villoresi, G.; Kopytenko, Y.A. Railway-generated magnetic ﬁeld: Environmental aspects.

Railw. Transp. Policies

Technol. Perspect.

2009,

87–140.

140. Bowman, J.D.; Methner, M.M. Hazard surveillance for industrial magnetic ﬁelds: II. Field characteristics from waveform

measurements.

Ann. Occup. Hyg.

2000,

44,

615–633. [CrossRef]

141. Methner, M.M.; Bowman, J.D. Hazard surveillance for industrial magnetic ﬁelds: I. Walkthrough survey of ambient ﬁelds and

sources.

Ann. Occup. Hyg.

2000,

44,

603–614. [CrossRef]

142. ILO.

Radiation Protection of Workers–Ionizing Radiation, ILO Code of Practice;

International Labour Ofﬁce: Geneva, Switzerland, 1987.

143. ICNIRP. Guidelines for limiting exposure to time-varying electric and magnetic ﬁelds (1 hz–100 khz).

Health Phys.

2010,

99,

818–836. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

37 of 55

144. Engel, C.L.; Rasanayagam, M.S.; Gray, J.M.; Rizzo, J. Work and female breast cancer: The state of the evidence, 2002–2017.

New

Solut.

2018,

28,

55–78. [CrossRef]

145. McCurdy, A.L.; Wijnberg, L.; Loomis, D.; Savitz, D.; Nylander-French, L.A. Exposure to extremely low frequency magnetic ﬁelds

among working women and homemakers.

Ann. Occup. Hyg.

2001,

45,

643–650. [CrossRef]

146. Li, P.; McLaughlin, J.; Infante-Rivard, C. Maternal occupational exposure to extremely low frequency magnetic ﬁelds and the risk

of brain cancer in the offspring.

Cancer Causes Control

2009,

20,

945–955. [CrossRef]

147. Sorahan, T. Magnetic ﬁelds and brain tumour risks in uk electricity supply workers.

Occup. Med.

2014,

64,

157–165. [CrossRef]

[PubMed]

148. Turner, M.C.; Benke, G.; Bowman, J.D.; Figuerola, J.; Fleming, S.; Hours, M.; Kincl, L.; Krewski, D.; McLean, D.; Parent, M.E.; et al.

Occupational exposure to extremely low-frequency magnetic ﬁelds and brain tumor risks in the interocc study.

Cancer Epidemiol.

Biomark. Prev.

2014,

23,

1863–1872. [CrossRef] [PubMed]

149. Oraby, T.; Sivaganesan, S.; Bowman, J.D.; Kincl, L.; Richardson, L.; McBride, M.; Siemiatycki, J.; Cardis, E.; Krewski, D.; On behalf

of the INTEROCC Study Group. Berkson error adjustment and other exposure surrogates in occupational case-control studies,

with application to the canadian interocc study.

J. Expo. Sci. Environ. Epidemiol.

2018,

28,

251–258. [CrossRef] [PubMed]

150. Carlberg, M.; Koppel, T.; Ahonen, M.; Hardell, L. Case-control study on occupational exposure to extremely low-frequency

electromagnetic ﬁelds and the association with meningioma.

BioMed Res. Int.

2018,

5912394. [CrossRef]

151. Goodman, M.; Kelsh, M.; Ebi, K.; Iannuzzi, J.; Langholz, B. Evaluation of potential confounders in planning a study of occupational

magnetic ﬁeld exposure and female breast cancer.

Epidemiology

2002,

13,

50–58. [CrossRef]

152. Kelsh, M.A.; Bracken, T.D.; Sahl, J.D.; Shum, M.; Ebi, K.L. Occupational magnetic ﬁeld exposures of garment workers: Results of

personal and survey measurements.

Bioelectromagnetics

2003,

24,

316–326. [CrossRef]

153. Juutilainen, J.; Kumlin, T. Occupational magnetic ﬁeld exposure and melatonin: Interaction with light-at-night.

Bioelectromagnetics

2006,

27,

423–426. [CrossRef]

154. Hardell, L.; Holmberg, B.; Malker, H.; Paulsson, L.E. Exposure to extremely-low-frequency electromagnetic-ﬁelds and the risk of

malignant diseases—An evaluation of epidemiologic and experimental ﬁndings.

Eur. J. Cancer Prev.

1995,

3–107. [CrossRef]

[PubMed]

155. McElroy, J.A.; Egan, K.M.; Titus-Ernstoff, L.; Anderson, H.A.; Trentham-Dietz, A.; Hampton, J.M.; Newcomb, P.A. Occupational

exposure to electromagnetic ﬁeld and breast cancer risk in a large, population based, case-control study in the united states.

Occup. Environ. Med.

2007,

49,

266–274. [CrossRef]

156. Grundy, A.; Harris, S.A.; Demers, P.A.; Johnson, K.C.; Agnew, D.A.; Villeneuve, P.J.; Canadian Canc Registries, E. Occupational

exposure to magnetic ﬁelds and breast cancer among canadian men.

Cancer Med.

2016,

586–596. [CrossRef]

157. Minder, C.E.; Pﬂuger, D.H. Leukemia, brain tumors, and exposure to extremely low frequency electromagnetic ﬁelds in swiss

railway employees.

Am. J. Epidemiol.

2001,

153,

825–835. [CrossRef] [PubMed]

158. Cam, S.T.; Firlarer, A.; Ozden, S.; Canseven, A.G.; Seyhan, N. Occupational exposure to magnetic ﬁelds from transformer stations

and electric enclosures in Turkey.

Electromagn. Biol. Med.

2011,

30,

74–79. [CrossRef] [PubMed]

159. Bethwaite, P.; Cook, A.; Kennedy, J.; Pearce, N. Acute leukemia in electrical workers: A new zealand case-control study.

Cancer

Causes Control

2001,

12,

683–689. [CrossRef] [PubMed]

160. Kheifets, L.; Monroe, J.; Vergara, X.; Mezei, G.; Aﬁﬁ, A.A. Occupational electromagnetic ﬁelds and leukemia and brain cancer: An

update to two meta-analyses.

J. Occup. Environ. Med.

2008,

50,

677–688. [CrossRef]

161. Roosli, M.; Lortscher, M.; Egger, M.; Pﬂuger, D.; Schreier, N.; Lortscher, E.; Locher, P.; Spoerri, A.; Minder, C. Leukaemia, brain

tumours and exposure to extremely low frequency magnetic ﬁelds: Cohort study of Swiss railway employees.

Occup. Environ.

Med.

2007,

64,

553–559. [CrossRef]

162. Huss, A.; Spoerri, A.; Egger, M.; Kromhout, H.; Vermeulen, R.; Swiss Natl, C. Occupational extremely low frequency magnetic

ﬁelds (elf-mf) exposure and hematolymphopoietic cancers—Swiss national cohort analysis and updated meta-analysis.

Environ.

Res.

2018,

164,

467–474. [CrossRef]

163. Nordenson, I.; Mild, K.H.; Jarventaus, H.; Hirvonen, A.; Sandstrom, M.; Wilen, J.; Blix, N.; Norppa, H. Chromosomal aberrations

in peripheral lymphocytes of train engine drivers.

Bioelectromagnetics

2001,

22,

306–315. [CrossRef]

164. Talibov, M.; Guxens, M.; Pukkala, E.; Huss, A.; Kromhout, H.; Slottje, P.; Martinsen, J.I.; Kjaerheim, K.; Sparen, P.; Weiderpass, E.;

et al. Occupational exposure to extremely low-frequency magnetic ﬁelds and electrical shocks and acute myeloid leukemia in

four nordic countries.

Cancer Causes Control

2015,

26,

1079–1085. [CrossRef]

165. Tynes, T.; Haldorsen, T. Residential and occupational exposure to 50 hz magnetic ﬁelds and hematological cancers in Norway.

Cancer Causes Control

2003,

14,

715–720. [CrossRef]

166. Harrington, J.M.; Nichols, L.; Sorahan, T.; van Tongeren, M. Leukaemia mortality in relation to magnetic ﬁeld exposure: Findings

from a study of united kingdom electricity generation and transmission workers, 1973–1997.

Occup. Environ. Med.

2001,

58,

307–314. [CrossRef]

167. Willett, E.V.; McKinney, P.A.; Fear, N.T.; Cartwright, R.A.; Roman, E. Occupational exposure to electromagnetic ﬁelds and acute

leukaemia: Analysis of a case-control study.

Occup. Environ. Med.

2003,

60,

577–583. [CrossRef] [PubMed]

168. Hug, K.; Grize, L.; Seidler, A.; Kaatsch, P.; Schuz, J. Parental occupational exposure to extremely low frequency magnetic ﬁelds

and childhood cancer: A german case-control study.

Am. J. Epidemiol.

2010,

171,

27–35. [CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

38 of 55

169. Talibov, M.; Olsson, A.; Bailey, H.; Erdmann, F.; Metayer, C.; Magnani, C.; Petridou, E.; Auvinen, A.; Spector, L.; Clavel, J.; et al.

Parental occupational exposure to low-frequency magnetic ﬁelds and risk of leukaemia in the offspring: Findings from the

childhood leukaemia international consortium (CLIC).

Occup. Environ. Med.

2019,

76,

746–753. [CrossRef]

170. Su, L.L.; Fei, Y.; Wei, X.X.; Guo, J.; Jiang, X.G.; Lu, L.Q.; Chen, G.D. Associations of parental occupational exposure to extremely

low-frequency magnetic ﬁelds with childhood leukemia risk.

Leuk. Lymphoma

2016,

57,

2855–2862. [CrossRef]

171. Hakansson, N.; Floderus, B.; Gustavsson, P.; Johansen, C.; Olsen, J.H. Cancer incidence and magnetic ﬁeld exposure in industries

using resistance welding in Sweden.

Occup. Environ. Med.

2002,

59,

481–486. [CrossRef]

172. Yousif, L.; Blettner, M.; Hammer, G.P.; Zeeb, H. Testicular cancer risk associated with occupational radiation exposure: A

systematic literature review.

J. Radiol. Prot.

2010,

30,

389–406. [CrossRef]

173. Karipidis, K.; Benke, G.; Sim, M.; Fritschi, L.; Yost, M.; Armstrong, B.; Hughes, A.M.; Grulich, A.; Vajdic, C.M.; Kaldor, J.M.; et al.

Occupational exposure to power frequency magnetic ﬁelds and risk of non-hodgkin lymphoma.

Occup. Environ. Med.

2007,

64,

25–29. [CrossRef]

174. Forssen, U.M.; Lonn, S.; Ahlbom, A.; Savitz, D.A.; Feychting, M. Occupational magnetic ﬁeld exposure and the risk of acoustic

neuroma.

Am. J. Ind. Med.

2006,

49,

112–118. [CrossRef]

175. Carlberg, M.; Koppel, T.; Ahonen, M.; Hardell, L. Case-control study on occupational exposure to extremely low-frequency

electromagnetic ﬁelds and the association with acoustic neuroma.

Environ. Res.

2020,

187,

109621. [CrossRef]

176. Lope, V.; Perez-Gomez, B.; Aragones, N.; Lopez-Abentes, G.; Gustavsson, P.; Floderus, B.; Dosemeci, M.; Silva, A.; Pollan, M.

Occupational exposure to ionizing radiation and electromagnetic ﬁelds in relation to the risk of thyroid cancer in Sweden.

Scand.

J. Work Environ. Health

2006,

32,

276–284. [CrossRef]

177. De Roos, A.J.; Teschke, K.; Savitz, D.A.; Poole, C.; Grufferman, S.; Pollock, B.H.; Olshan, A.F. Parental occupational exposures to

electromagnetic ﬁelds and radiation and the incidence of neuroblastoma in offspring.

Epidemiology

2001,

12,

508–517. [CrossRef]

[PubMed]

178. Sahl, J.; Mezei, G.; Kavet, R.; McMillan, A.; Silvers, A.; Sastre, A.; Kheifets, L. Occupational magnetic ﬁeld exposure and

cardiovascular mortality in a cohort of electric utility workers.

Am. J. Epidemiol.

2002,

156,

913–918. [CrossRef] [PubMed]

179. Lange, S.; Viergutz, T.; Simko, M. Modiﬁcations in cell cycle kinetics and in expression of g(1) phase-regulating proteins in human

amniotic cells after exposure to electromagnetic ﬁelds and ionizing radiation.

Cell Prolif.

2004,

37,

337–349. [CrossRef] [PubMed]

180. Huang, L.Z.; Dong, L.A.; Chen, Y.T.; Qi, H.S.; Xiao, D.M. Effects of sinusoidal magnetic ﬁeld observed on cell proliferation, ion

concentration, and osmolarity in two human cancer cell lines.

Electromagn. Biol. Med.

2006,

25,

113–126. [CrossRef] [PubMed]

181. Buemi, M.; Marino, D.; Di Pasquale, G.; Floccari, F.; Senatore, M.; Aloisi, C.; Grasso, F.; Mondio, G.; Perillo, P.; Frisina, N.; et al.

Cell proliferation/cell death balance in renal cell cultures after exposure to a static magnetic ﬁeld.

Nephron

2001,

87,

269–273.

[CrossRef]

182. Sztafrowski, D.; Jazwiec, B.; Gumiela, J.; Kuliczkowski, K. Inﬂuence of north and south poles of static magnetic ﬁeld (SMF) on

apoptosis of hl60 cell line.

Prz. Elektrotech.

2018,

94,

182–185.

183. Tenuzzo, B.; Chionna, A.; Panzarini, E.; Lanubile, R.; Tarantino, P.; Di Jeso, B.; Dwikat, M.; Dini, L. Biological effects of 6 mt static

magnetic ﬁelds: A comparative study in different cell types.

Bioelectromagnetics

2006,

27,

560–577. [CrossRef]

184. Naziroglu, M.; Clg, B.; Dogan, S.; Uguz, A.C.; Dilek, S.; Faouzi, D. 2.45-gz wireless devices induce oxidative stress and proliferation

through cytosolic ca2+ inﬂux in human leukemia cancer cells.

Int. J. Radiat. Biol.

2012,

88,

449–456. [CrossRef]

185. Barati, M.; Javidi, M.A.; Darvishi, B.; Shariatpanahi, S.P.; Moosavi, Z.S.M.; Ghadirian, R.; Khani, T.; Sanati, H.; Simaee, H.;

Barough, M.S.; et al. Necroptosis triggered by ros accumulation and Ca

overload, partly explains the inﬂammatory responses

and anti-cancer effects associated with 1 hz, 100 mt elf-mf in vivo.

Free Radic. Biol. Med.

2021,

169,

84–98. [CrossRef]

186. Rosen, A.D. Mechanism of action of moderate-intensity static magnetic ﬁelds on biological systems.

Cell Biochem. Biophys.

2003,

39,

163–173. [CrossRef]

187. Wei, J.; Sun, J.; Xu, H.; Shi, L.; Sun, L.; Zhang, J. Effects of extremely low frequency electromagnetic ﬁelds on intracellular calcium

transients in cardiomyocytes.

Electromagn. Biol. Med.

2015,

34,

77–84. [CrossRef] [PubMed]

188. Chen, Y.C.; Chen, C.C.; Tu, W.; Cheng, Y.T.; Tseng, F.G. Design and fabrication of a microplatform for the proximity effect study of

localized elf-EMF on the growth of in vitro hela and pc-12 cells.

J. Micromech. Microeng.

2010,

20,

125023. [CrossRef]

189. Bae, J.E.; Do, J.Y.; Kwon, S.H.; Lee, S.D.; Jung, Y.W.; Kim, S.C.; Chae, K.S. Electromagnetic ﬁeld-induced converse cell growth

during a long-term observation.

Int. J. Radiat. Biol.

2013,

89,

1035–1044. [CrossRef] [PubMed]

190. Richard, D.; Lange, S.; Viergutz, T.; Kriehuber, R.; Weiss, D.G.; Simko, M. Inﬂuence of 50 hz electromagnetic ﬁelds in combination

with a tumour promoting phorbol ester on protein kinase c and cell cycle in human cells.

Mol. Cell. Biochem.

2002,

232,

133–141.

[CrossRef] [PubMed]

191. Kapri-Pardes, E.; Hanoch, T.; Maik-Rachline, G.; Murbach, M.; Bounds, P.L.; Kuster, N.; Seger, R. Activation of signaling cascades

by weak extremely low frequency electromagnetic ﬁelds.

Cell. Physiol. Biochem.

2017,

43,

1533–1546. [CrossRef] [PubMed]

192. Kirschenlohr, H.; Ellis, P.; Hesketh, R.; Metcalfe, J. Gene expression proﬁles in white blood cells of volunteers exposed to a 50 hz

electromagnetic ﬁeld.

Radiat. Res.

2012,

178,

138–149. [CrossRef] [PubMed]

193. Kabacik, S.; Kirschenlohr, H.; Raffy, C.; Whitehill, K.; Coster, M.; Abe, M.; Brindle, K.; Badie, C.; Sienkiewicz, Z.; Boufﬂer, S.

Investigation of transcriptional responses of juvenile mouse bone marrow to power frequency magnetic ﬁelds.

Mutat. Res./Fundam.

Mol. Mech. Mutagenesis

2013,

745,

40–45. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

39 of 55

194. Panagopoulos, D.J.; Karabarbounis, A.; Lioliousis, C. Elf alternating magnetic ﬁeld decreases reproduction by DNA damage

induction.

Cell Biochem. Biophys.

2013,

67,

703–716. [CrossRef]

195. Pardo, J.C.T.; Grimaldi, S.; Taranta, M.; Naldi, I.; Cinti, C. Microwave electromagnetic ﬁeld regulates gene expression in

t-lymphoblastoid leukemia ccrf-cem cell line exposed to 900 mhz.

Electromagn. Biol. Med.

2012,

31,

1–18. [CrossRef]

196. Zhou, J.L.; Li, C.L.; Yao, G.D.; Chiang, H.A.; Chang, Z.L. Gene expression of cytokine receptors in hl60 cells exposed to a 50 hz

magnetic ﬁeld.

Bioelectromagnetics

2002,

23,

339–346. [CrossRef]

197. Diab, K.A. The impact of the low frequency of the electromagnetic ﬁeld on human. In

Cell Biology and Translational Medicine,

Volume 7: Stem Cells and Therapy: Emerging Approaches;

Turksen, K., Ed.; Springer: Berlin/Heidelberg, Germany, 2020; Volume

1237, pp. 135–149.

198. Giorgi, G.; Del Re, B. Epigenetic dysregulation in various types of cells exposed to extremely low-frequency magnetic ﬁelds.

Cell

Tissue Res

2021,

386,

1–15. [CrossRef] [PubMed]

199. Harada, S.; Yamada, S.; Kuramata, O.; Gunji, Y.; Kawasaki, M.; Miyakawa, T.; Yonekura, H.; Sakurai, S.; Bessho, K.; Hosono, R.;

et al. Effects of high elf magnetic ﬁelds on enzyme-catalyzed DNA and RNA synthesis in vitro and on a cell-free DNA mismatch

repair.

Bioelectromagnetics

2001,

22,

260–266. [CrossRef] [PubMed]

200. Li, H.L.; Lin, L.; Li, L.; Zhou, L.; Zhang, Y.; Hao, S.; Ding, Z.H. Exosomal small rna sequencing uncovers the microrna dose

markers for power frequency electromagnetic ﬁeld exposure.

Biomarkers

2018,

23,

315–327. [CrossRef] [PubMed]

201. Manser, M.; Sater, M.R.A.; Schmid, C.D.; Noreen, F.; Murbach, M.; Kuster, N.; Schuermann, D.; Schar, P. ELF-MF exposure affects

the robustness of epigenetic programming during granulopoiesis.

Sci. Rep.

2017,

43345. [CrossRef]

202. Sun, W.J.; Gan, Y.P.; Fu, Y.T.; Lu, D.Q.; Chiang, H. An incoherent magnetic ﬁeld inhibited EGF receptor clustering and phosphory-

lation induced by a 50-hz magnetic ﬁeld in cultured FL cells.

Cell. Physiol. Biochem.

2008,

22,

507–514. [CrossRef]

203. Ke, X.Q.; Sun, W.J.; Lu, D.Q.; Fu, Y.T.; Chiang, H. 50-hz magnetic ﬁeld induces EGF-receptor clustering and activates RAS.

Int. J.

Radiat. Biol.

2008,

84,

413–420. [CrossRef] [PubMed]

204. Eleuteri, A.M.; Amici, M.; Bonﬁli, L.; Cecarini, V.; Cuccioloni, M.; Grimaldi, S.; Giuliani, L.; Angeletti, M.; Fioretti, E. 50 hz

extremely low frequency electromagnetic ﬁelds enhance protein carbonyl groups content in cancer cells: Effects on proteasomal

systems.

J. Biomed. Biotechnol.

2009,

834239. [CrossRef]

205. Testa, A.; Cordelli, E.; Stronati, L.; Marino, C.; Lovisolo, G.A.; Fresegna, A.M.; Conti, D.; Villani, P. Evaluation of genotoxic effect

of low level 50 hz magnetic ﬁelds on human blood cells using different cytogenetic assays.

Bioelectromagnetics

2004,

25,

613–619.

[CrossRef]

206. Stronati, L.; Testa, A.; Villani, R.; Marino, C.; Lovisolo, G.A.; Conti, D.; Russo, F.; Fresegna, A.M.; Cordelli, E. Absence of

genotoxicity in human blood cells exposed to 50 hz magnetic ﬁelds as assessed by comet assay, chromosome aberration,

micronucleus, and sister chromatid exchange analyses.

Bioelectromagnetics

2004,

25,

41–48. [CrossRef]

207. Kayhan, H.; Erdebilli, B.; Gonen, S.; Esmekaya, M.A.; Ertekin, E.; Canseven, A.G. Effects of extremely low-frequency magnetic

ﬁeld on healthy ﬁbroblasts and breast cancer cells.

J. Istanb. Fac. Med.

2020,

83,

384–389. [CrossRef]

208. Kim, J.; Ha, C.S.; Lee, H.J.; Song, K. RePETitive exposure to a 60-hz time-varying magnetic ﬁeld induces DNA double-strand

breaks and apoptosis in human cells.

Biochem. Biophys. Res. Commun.

2010,

400,

739–744. [CrossRef] [PubMed]

209. Supino, R.; Bottone, M.G.; Pellicciari, C.; Caserini, C.; Bottiroli, G.; Belleri, M.; Veicsteinas, A. Sinusoidal 50 hz magnetic ﬁelds do

not affect structural morphology and proliferation of human cells in vitro.

Histol. Histopathol.

2001,

16,

719–726.

210. Savage, R.E.; Kanitz, M.H.; Lotz, W.G.; Conover, D.; Hennessey, E.M.; Hanneman, W.H.; Witzmann, F.A. Changes in gene and

protein expression in magnetic ﬁeld-treated human glioma cells.

Toxicol. Mech. Methods

2005,

15,

115–120. [CrossRef] [PubMed]

211. Kanitz, M.H.; Witzmann, F.A.; Lotz, W.G.; Conover, D.; Savage, R.E. Investigation of protein expression in magnetic ﬁeld-treated

human glioma cells.

Bioelectromagnetics

2007,

28,

546–552. [CrossRef] [PubMed]

212. Yoshizawa, H.; Tsuchiya, T.; Mizoe, H.; Ozeki, H.; Kanao, S.; Yomori, H.; Sakane, C.; Hasebe, S.; Motomura, T.; Yamakawa, T.; et al.

No effect of extremely low-frequency magnetic ﬁeld observed on cell growth or initial response of cell proliferation in human

cancer cell lines.

Bioelectromagnetics

2002,

23,

355–368. [CrossRef] [PubMed]

213. Calcabrini, C.; Mancini, U.; De Bellis, R.; Diaz, A.R.; Martinelli, M.; Cucchiarini, L.; Sestili, P.; Stocchi, V.; Potenza, L. Effect of

extremely low-frequency electromagnetic ﬁelds on antioxidant activity in the human keratinocyte cell line nctc 2544.

Biotechnol.

Appl. Biochem.

2017,

64,

415–422. [CrossRef]

214. Kim, K.; Lee, Y.S.; Kim, N.; Choi, H.D.; Kang, D.J.; Kim, H.R.; Lim, K.M. Effects of electromagnetic waves with LTE and 5g

bandwidth on the skin pigmentation in vitro.

Int. J. Mol. Sci.

2021,

22,

170. [CrossRef]

215. Jandova, A.; Pokorny, J.; Cocek, A.; Trojan, S.; Nedbalova, M.; Dohnalova, A. Effects of sinusoidal 0.5 mt magnetic ﬁeld on

leukocyte adherence inhibition.

Electromagn. Biol. Med.

2004,

23,

81–96.

216. Jandova, A.; Hurych, J.; Pokorny, J.; Cocek, A.; Trojan, S.; Nedbalova, M.; Dohnalova, A. Effects of sinusoidal magnetic ﬁeld on

adherence inhibition of leukocytes.

Electro- Magn.

2001,

20,

397–413. [CrossRef]

217. Kaszuba-Zwoinska, J.; Zdzilowska, E.; Chorobik, P.; Slodowska-Hajduk, Z.; Juszczak, K.; Zaraska, W.; Thor, P.J. Pulsing

electromagnetic ﬁeld and death of proliferating peripheral blood mononuclear cells from patients with acute myelogenic

leukemia.

Adv. Clin. Exp. Med.

2011,

20,

721–727.

218. Mangiacasale, R.; Tritarelli, A.; Sciamanna, I.; Cannone, M.; Lavia, P.; Barberis, M.C.; Lorenzini, R.; Cundari, E. Normal and

cancer-prone human cells respond differently to extremely low frequency magnetic ﬁelds.

FEBS Lett.

2001,

487,

397–403.

[CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

40 of 55

219. Kaszuba-Zwoinska, J.; Wojcik, K.; Bereta, M.; Ziomber, A.; Pierzchalski, P.; Rokita, E.; Marcinkiewicz, J.; Zaraska, W.; Thor, P.

Pulsating electromagnetic ﬁeld stimulation prevents cell death of puromycin treated u937 cell line.

J. Physiol. Pharmacol.

2010,

61,

201–205. [PubMed]

220. Hirose, H.; Nakahara, T.; Zhang, Q.M.; Yonei, S.; Miyakoshi, J. Static magnetic ﬁeld with a strong magnetic ﬁeld gradient (41.7

t/m) induces c-jun expression in hl-60 cells.

In Vitro Cell. Dev. Biol.-Anim.

2003,

39,

348–352. [CrossRef]

221. Golbach, L.A.; Philippi, J.G.M.; Cuppen, J.J.M.; Savelkoul, H.F.J.; Verburg-van Kemenade, B.M.L. Calcium signalling in human

neutrophil cell lines is not affected by low-frequency electromagnetic ﬁelds.

Bioelectromagnetics

2015,

36,

430–443. [CrossRef]

[PubMed]

222. Yan, J.H.; Dong, L.A.; Zhang, B.H.; Qi, N.M. Effects of extremely low-frequency magnetic ﬁeld on growth and differentiation of

human mesenchymal stem cells.

Electromagn. Biol. Med.

2010,

29,

165–176. [CrossRef] [PubMed]

223. Santini, M.T.; Rainaldi, G.; Ferrante, A.; Indovina, P.L.; Vecchia, P.; Donelli, G. Effects of a 50 hz sinusoidal magnetic ﬁeld on cell

adhesion molecule expression in two human osteosarcoma cell lines (mg-63 and saos-2).

Bioelectromagnetics

2003,

24,

327–338.

[CrossRef]

224. Saunders, R. Static magnetic ﬁelds: Animal studies.

Prog. Biophys. Mol. Biol.

2005,

87,

225–239. [CrossRef]

225. Mild, K.H.; Mattsson, M.O. Elf noise ﬁelds: A review.

Electromagn. Biol. Med.

2010,

29,

72–97. [CrossRef]

226. Marino, A.A.; Wolcott, R.M.; Chervenak, R.; Jourd’heuil, F.; Nilsen, E.; Frilot, C. Nonlinear dynamical law governs magnetic ﬁeld

induced changes in lymphoid phenotype.

Bioelectromagnetics

2001,

22,

529–546. [CrossRef] [PubMed]

227. Anderson, L.E.; Morris, J.E.; Sasser, L.B.; Loscher, W. Effects of 50-or 60-hertz, 100 mu t magnetic ﬁeld exposure in the dmba

mammary cancer model in sprague-dawley rats: Possible explanations for different results from two laboratories.

Environ. Health

Perspect.

2000,

108,

797–802.

228. Maruvada, P.S.; Harvey, S.M.; Jutras, P.; Goulet, D.; Mandeville, R. A magnetic ﬁeld exposure facility for evaluation of animal

carcinogenicity.

Bioelectromagnetics

2000,

21,

432–438. [CrossRef]

229. Loscher, W. Do cocarcinogenic effects of elf electromagnetic ﬁelds require repeated long-term interaction with carcinogens?

Characteristics of positive studies using the dmba breast cancer model in rats.

Bioelectromagnetics

2001,

22,

603–614. [CrossRef]

[PubMed]

230. Fedrowitz, M.; Kamino, K.; Loscher, W. Signiﬁcant differences in the effects of magnetic ﬁeld exposure on 7,12-dimethylbenz(a)anthracene-

induced mammary carcinogenesis in two substrains of sprague-dawley rats.

Cancer Res.

2004,

64,

243–251. [CrossRef] [PubMed]

231. Galloni, P.; Marino, C. Effects of 50 hz magnetic ﬁeld exposure on tumor experimental models.

Bioelectromagnetics

2000,

21,

608–614. [CrossRef]

232. McLean, J.R.; Thansandote, A.; McNamee, J.P.; Tryphonas, L.; Lecuyer, D.; Gajda, G. A 60 hz magnetic ﬁeld does not affect the

incidence of squamous cell carcinomas in sencar mice.

Bioelectromagnetics

2003,

24,

75–81. [CrossRef]

233. McNamee, J.P.; Bellier, P.V.; McLean, J.R.N.; Marro, L.; Gajda, G.B.; Thansandote, A. DNA damage and apoptosis in the immature

mouse cerebellum after acute exposure to a 1 mt, 60 hz magnetic ﬁeld.

Mutat. Res. Genet. Toxicol. Environ. Mutagenesis

2002,

513,

121–133. [CrossRef]

234. McNamee, J.P.; Bellier, P.V.; Chauhan, V.; Gajda, G.B.; Lemay, E.; Thansandote, A. Evaluating DNA damage in rodent brain after

acute 60 hz magnetic-ﬁeld exposure.

Radiat. Res.

2005,

164,

791–797. [CrossRef]

235. Luo, X.; Jia, S.J.; Li, R.Y.; Gao, P.; Zhang, Y.W. Occupational exposure to 50 hz magnetic ﬁelds does not alter responses of

inﬂammatory genes and activation of splenic lymphocytes in mice.

Int. J. Occup. Med. Environ. Health

2016,

29,

277–291.

[CrossRef]

236. Sommer, A.M.; Lerchl, A. 50 hz magnetic ﬁelds of 1 mt do not promote lymphoma development in AKR/J mice.

Radiat. Res.

2006,

165,

343–349. [CrossRef]

237. Tatarov, I.; Panda, A.; Petkov, D.; Kolappaswamy, K.; Thompson, K.; Kavirayani, A.; Lipsky, M.M.; Elson, E.; Davis, C.C.; Martin,

S.S.; et al. Effect of magnetic ﬁelds on tumor growth and viability.

Comp. Med.

2011,

61,

339–345.

238. Vallejo, D.; Hidalgo, M.A.; Hernandez, J.M. Effects of long-term exposure to an extremely low frequency magnetic ﬁeld (15

microt) on selected blood coagulation variables in of1 mice.

Electromagn. Biol. Med.

2019,

38,

279–286. [CrossRef] [PubMed]

239. Cabrales, L.B.; Ciria, H.C.; Bruzon, R.P.; Quevedo, M.S.; Cespedes, M.C.; Salas, M.F. Elf magnetic ﬁeld effects on some hematologi-

cal and biochemical parameters of peripheral blood in mice.

Electro- Magn.

2001,

20,

185–191. [CrossRef]

240. Qi, G.Y.; Zuo, X.X.; Zhou, L.H.; Aoki, E.; Okamula, A.; Watanebe, M.; Wang, H.P.; Wu, Q.H.; Lu, H.L.; Tuncel, H.; et al. Effects of

extremely low-frequency electromagnetic ﬁelds (elf-EMF) exposure on b6c3f1 mice.

Environ. Health Prev. Med.

2015,

20,

287–293.

[CrossRef]

241. Salim, E.I.; Omar, K.M.; Abou-Hattab, H.A.; Abou-Zaid, F.A. Pituitary toxicity but lack of rat colon carcinogenicity of a dc-magnetic

ﬁeld in a medium-term bioassay.

Asian Pac. J. Cancer Prev.

2008,

131–140.

242. Anderson, L.E.; Morris, J.E.; Miller, D.L.; Rafferty, C.N.; Ebi, K.L.; Sasser, L.B. Large granular lymphocytic (lgl) leukemia in rats

exposed to intermittent 60 hz magnetic ﬁelds.

Bioelectromagnetics

2001,

22,

185–193. [CrossRef] [PubMed]

243. Lee, H.J.; Choi, S.Y.; Jang, J.J.; Gimm, Y.M.; Pack, J.K.; Choi, H.D.; Kim, N.; Lee, Y.S. Lack of promotion of mammary, lung and

skin tumorigenesis by 20 khz triangular magnetic ﬁelds.

Bioelectromagnetics

2007,

28,

446–453. [CrossRef] [PubMed]

244. Tuncel, H.; Shimamoto, F.; Cagatay, P.; Kalkan, M.T. Variable e-cadherin expression in a mnu-induced colon tumor model in rats

which exposed with 50 hz frequency sinusoidal magnetic ﬁeld.

Tohoku J. Exp. Med.

2002,

198,

245–249. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

41 of 55

245. Fedrowitz, M.; Loscher, W. Exposure of ﬁscher 344 rats to a weak power frequency magnetic ﬁeld facilitates mammary tumorige-

nesis in the dmba model of breast cancer.

Carcinogenesis

2008,

29,

186–193. [CrossRef]

246. Ushio-Fukai, M.; Ash, D.; Nagarkoti, S.; de Chantemele, E.J.B.; Fulton, D.J.R.; Fukai, T. Interplay between reactive oxygen/reactive

nitrogen species and metabolism in vascular biology and disease.

Antioxid. Redox Signal.

2021,

34,

1319–1354. [CrossRef] [PubMed]

247. Sies, H. Oxidative stress: A concept in redox biology and medicine.

Redox Biol.

2015,

180–183. [CrossRef]

248. Azab, A.E.; Ebrahim, S.A. Exposure to electromagnetic ﬁelds induces oxidative stress and pathophysiological changes in the

cardiovascular system.

J. Appl. Biotechnol. Bioeng.

2017,

96–102. [CrossRef]

249. Schuermann, D.; Mevissen, M. Manmade electromagnetic ﬁelds and oxidative stress-biological effects and consequences for

health.

Int. J. Mol. Sci.

2021,

22,

3772. [CrossRef] [PubMed]

250. Havas, M. When theory and observation collide: Can non-ionizing radiation cause cancer?

Environ. Pollut.

2017,

221,

501–505.

[CrossRef] [PubMed]

251. Hore, P.J.; Ivanov, K.L.; Wasielewski, M.R. Spin chemistry.

J. Chem. Phys.

2020,

152,

120401. [CrossRef]

252. Jones, A.R. Magnetic ﬁeld effects in proteins.

Mol. Phys.

2016,

114,

1691–1702. [CrossRef]

253. Mattsson, M.O.; Simkó, M. Grouping of experimental conditions as an approach to evaluate effects of extremely low-frequency

magnetic ﬁelds on oxidative response in in vitro studies.

Front. Public Health

2014,

132. [CrossRef]

254. Juutilainen, J.; Herrala, M.; Luukkonen, J.; Naarala, J.; Hore, P.J. Magnetocarcinogenesis: Is there a mechanism for carcinogenic

effects of weak magnetic ﬁelds?

Proc. Biol. Sci.

2018,

285,

20180590. [CrossRef]

255. Höytö, A.; Herrala, M.; Luukkonen, J.; Juutilainen, J.; Naarala, J. Cellular detection of 50 hz magnetic ﬁelds and weak blue light:

Effects on superoxide levels and genotoxicity.

Int. J. Radiat. Biol.

2017,

93,

646–652. [CrossRef]

256. Orel, V.E.; Krotevych, M.; Dasyukevich, O.; Rykhalskyi, O.; Syvak, L.; Tsvir, H.; Tsvir, D.; Garmanchuk, L.; Orel Vcapital Ve, C.;

Sheina, I.; et al. Effects induced by a 50 hz electromagnetic ﬁeld and doxorubicin on walker-256 carcinosarcoma growth and

hepatic redox state in rats.

Electromagn. Biol. Med.

2021,

40,

475–487. [CrossRef]

257. Mannerling, A.C.; Simko, M.; Mild, K.H.; Mattsson, M.O. Effects of 50-hz magnetic ﬁeld exposure on superoxide radical anion

formation and hsp70 induction in human k562 cells.

Radiat. Environ. Biophys.

2010,

49,

731–741. [CrossRef]

258. Bułdak, R.J.; Polaniak, R.; Bułdak, Ł.; Zwirska-Korczala, K.; Skonieczna, M.; Monsiol, A.; Kukla, M.; Duława-Bułdak, A.; Birkner,

E. Short-term exposure to 50 hz elf-EMF alters the cisplatin-induced oxidative response in at478 murine squamous cell carcinoma

cells.

Bioelectromagnetics

2012,

33,

641–651. [CrossRef] [PubMed]

259. Luukkonen, J.; Liimatainen, A.; Juutilainen, J.; Naarala, J. Induction of genomic instability, oxidative processes, and mitochondrial

activity by 50hz magnetic ﬁelds in human sh-sy5y neuroblastoma cells.

Mutat. Res./Fundam. Mol. Mech. Mutagenesis

2014,

760,

33–41. [CrossRef]

260. Xu, A.; Wang, Q.; Lin, T. Low-frequency magnetic ﬁelds (lf-mfs) inhibit proliferation by triggering apoptosis and altering cell

cycle distribution in breast cancer cells.

Int. J. Mol. Sci.

2020,

21,

2952. [CrossRef] [PubMed]

261. Cios, A.; Ciepielak, M.; Stankiewicz, W.; Szymanski, L. The inﬂuence of the extremely low frequency electromagnetic ﬁeld on

clear cell renal carcinoma.

Int. J. Mol. Sci.

2021,

22,

1342. [CrossRef] [PubMed]

262. Amara, S.; Abdelmelek, H.; Garrel, C.; Guiraud, P.; Douki, T.; Ravanat, J.L.; Favier, A.; Sakly, M.; Ben Rhouma, K. Zinc

supplementation ameliorates static magnetic ﬁeld-induced oxidative stress in rat tissues.

Environ. Toxicol. Pharmacol.

2007,

23,

193–197. [CrossRef] [PubMed]

263. Amara, S.; Abdelmelek, H.; Garrel, C.; Guiraud, P.; Douki, T.; Ravanat, J.L.; Favier, A.; Sakly, M.; Ben Rhouma, K. Inﬂuence of

static magnetic ﬁeld on cadmium toxicity: Study of oxidative stress and DNA damage in rat tissues.

J. Trace Elem. Med. Biol.

2006,

20,

263–269. [CrossRef] [PubMed]

264. Amara, S.; Douki, T.; Garel, C.; Favier, A.; Sakly, M.; Rhouma, K.B.; Abdelmelek, H. Effects of static magnetic ﬁeld exposure on

antioxidative enzymes activity and DNA in rat brain.

Gen. Physiol. Biophys.

2009,

28,

260–265. [CrossRef]

265. Amara, S.; Douki, T.; Ravanat, J.L.; Garrel, C.; Guiraud, P.; Favier, A.; Sakly, M.; Ben Rhouma, K.; Abdelmelek, H. Inﬂuence of a

static magnetic ﬁeld (250 mt) on the antioxidant response and DNA integrity in thp1 cells.

Phys. Med. Biol.

2007,

52,

889–898.

[CrossRef]

266. Reale, M.; Kamal, M.A.; Patruno, A.; Costantini, E.; D’Angelo, C.; Pesce, M.; Greig, N.H. Neuronal cellular responses to extremely

low frequency electromagnetic ﬁeld exposure: Implications regarding oxidative stress and neurodegeneration.

PLoS ONE

2014,

e104973. [CrossRef]

267. Patruno, A.; Tabrez, S.; Pesce, M.; Shakil, S.; Kamal, M.A.; Reale, M. Effects of extremely low frequency electromagnetic ﬁeld

(elf-EMF) on catalase, cytochrome p450 and nitric oxide synthase in erythro-leukemic cells.

Life Sci.

2015,

121,

117–123. [CrossRef]

268. Lee, B.C.; Johng, H.M.; Lim, J.K.; Jeong, J.H.; Baik, K.Y.; Nam, T.J.; Lee, J.H.; Kim, J.; Sohn, U.D.; Yoon, G.; et al. Effects of extremely

low frequency magnetic ﬁeld on the antioxidant defense system in mouse brain: A chemiluminescence study.

J. Photochem.

Photobiol. B Biol.

2004,

73,

43–48. [CrossRef] [PubMed]

269. Lewicka, M.; Henrykowska, G.A.; Pacholski, K.; Smigielski, J.; Rutkowski, M.; Dziedziczak-Buczynska, M.; Buczynski, A. The

effect of electromagnetic radiation emitted by display screens on cell oxygen metabolism—In vitro studies.

Arch. Med. Sci.

2015,

11,

1330–1339. [CrossRef] [PubMed]

270. Zwirska-Korczala, K.; Adamczyk-Sowa, M.; Polaniak, R.; Sowa, P.; Birkner, E.; Drzazga, Z.; Brzozowski, T.; Konturek, S.J.

Inﬂuence of extremely-low-frequency magnetic ﬁeld on antioxidative melatonin properties in at478 murine squamous cell

carcinoma culture.

Biol. Trace Elem. Res.

2004,

102,

227–243. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

42 of 55

271. Zhao, B.; Yu, T.; Wang, S.; Che, J.; Zhou, L.; Shang, P. Static magnetic ﬁeld (0.2–0.4 t) stimulates the self-renewal ability of

osteosarcoma stem cells through autophagic degradation of ferritin.

Bioelectromagnetics

2021,

42,

371–383. [CrossRef]

272. Kesari, K.K.; Juutilainen, J.; Luukkonen, J.; Naarala, J. Induction of micronuclei and superoxide production in neuroblastoma and

glioma cell lines exposed to weak 50 hz magnetic ﬁelds.

J. R. Soc. Interface

2016,

13,

20150995. [CrossRef]

273. Erdal, N.; Gürgül, S.; Tamer, L.; Ayaz, L. Effects of long-term exposure of extremely low frequency magnetic ﬁeld on oxida-

tive/nitrosative stress in rat liver.

J. Radiat. Res.

2008,

49,

181–187. [CrossRef]

274. Koh, E.K.; Ryu, B.K.; Jeong, D.Y.; Bang, I.S.; Nam, M.H.; Chae, K.S. A 60-hz sinusoidal magnetic ﬁeld induces apoptosis of

prostate cancer cells through reactive oxygen species.

Int. J. Radiat. Biol.

2008,

84,

945–955. [CrossRef]

275. Kahya, M.C.; Nazıroglu, M.; Çig, B. Selenium reduces mobile phone (900 mhz)-induced oxidative stress, mitochondrial function,

and apoptosis in breast cancer cells.

Biol. Trace Elem. Res.

2014,

160,

285–293. [CrossRef]

276. Polaniak, R.; Buldak, R.J.; Karon, M.; Birkner, K.; Kukla, M.; Zwirska-Korczala, K.; Birkner, E. Inﬂuence of an extremely low

frequency magnetic ﬁeld (elf-EMF) on antioxidative vitamin e properties in at478 murine squamous cell carcinoma culture

in vitro.

Int. J. Toxicol.

2010,

29,

221–230. [CrossRef]

277. Bekhite, M.M.; Finkensieper, A.; Abou-Zaid, F.A.; El-Shourbagy, I.K.; El-Fiky, N.K.; Omar, K.M.; Sauer, H.; Wartenberg, M.

Differential effects of high and low strength magnetic ﬁelds on mouse embryonic development and vasculogenesis of embryonic

stem cells.

Reprod. Toxicol.

2016,

65,

46–58. [CrossRef]

278. Kang, S.K. Measuring the value of MRI: Comparative effectiveness & outcomes research.

J. Magn. Reson. Imaging

2019,

49,

e78–e84. [PubMed]

279. van der Heide, U.A.; Frantzen-Steneker, M.; Astreinidou, E.; Nowee, M.E.; van Houdt, P.J. MRI basics for radiation oncologists.

Clin. Transl. Radiat. Oncol.

2019,

18,

74–79. [CrossRef] [PubMed]

280. Raaijmakers, A.J.E.; Raaymakers, B.W.; Lagendijk, J.J.W. Magnetic-ﬁeld-induced dose effects in mr-guided radiotherapy systems:

Dependence on the magnetic ﬁeld strength.

Phys. Med. Biol.

2008,

53,

909–923. [CrossRef] [PubMed]

281. Morsing, A.; Hildebrandt, M.G.; Vilstrup, M.H.; Wallenius, S.E.; Gerke, O.; Petersen, H.; Johansen, A.; Andersen, T.L.; Hoilund-

Carlsen, P.F. Hybrid PET/MRI in major cancers: A scoping review.

Eur. J. Nucl. Med. Mol. Imaging

2019,

46,

2138–2151.

[CrossRef]

282. Scheenen, T.W.J.; Zamecnik, P. The role of magnetic resonance imaging in (future) cancer staging note the nodes.

Investig. Radiol.

2021,

56,

42–49. [CrossRef]

283. Avasthi, A.; Caro, C.; Pozo-Torres, E.; Leal, M.P.; Garcia-Martin, M.L. Magnetic nanoparticles as MRI contrast agents.

Top. Curr.

Chem.

2020,

378,

43.

284. Poku, L.O.; Cheng, Y.N.; Wang, K.; Sun, X.L. Na-23-MRI as a noninvasive biomarker for cancer diagnosis and prognosis.

J. Magn.

Reson. Imaging

2021,

53,

995–1014. [CrossRef]

285. Visser, M.; Muller, D.M.J.; van Duijn, J.M.; Smits, M.; Verburg, N.; Hendriks, E.J.; Nabuurs, R.J.A.; Bot, J.C.J.; Eijgelaar, R.S.; Witte,

M.; et al. Inter-rater agreement in glioma segmentations on longitudinal MRI.

NeuroImage Clin.

2019,

22,

101727. [CrossRef]

286. Patil, R.; Ljubimov, A.V.; Gangalum, P.R.; Ding, H.; Portilla-Arias, J.; Wagner, S.; Inoue, S.; Konda, B.; Rekechenetskiy, A.;

Chesnokova, A.; et al. MRI virtual biopsy and treatment of brain metastatic tumors with targeted nanobioconjugates: Nanoclinic

in the brain.

ACS Nano

2015,

5594–5608. [CrossRef]

287. Lasocki, A.; Anjari, M.; Kokurcan, S.O.; Thust, S.C. Conventional MRI features of adult diffuse glioma molecular subtypes: A

systematic review.

Neuroradiology

2021,

63,

353–362. [CrossRef]

288. Machhale, K.; Nandpuru, H.B.; Kapur, V.; Kosta, L. MRI brain cancer classiﬁcation using hybrid classiﬁer (SVM-KNN). In

Proceedings of the 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 28–30 May 2015;

pp. 60–65.

289. Nandpuru, H.B.; Salankar, S.S.; Bora, V.R. MRI brain cancer classiﬁcation using support vector machine. In Proceedings of the

2014 IEEE Students’ Conference on Electrical, Electronics and Computer Science, Bhopal, India, 1–2 March 2014.

290. Naser, M.A.; Deen, M.J. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images.

Comput. Biol. Med.

2020,

121,

8. [CrossRef] [PubMed]

291. Clement, P.; Booth, T.; Borovecki, F.; Emblem, K.E.; Figueiredo, P.; Hirschler, L.; Jancalek, R.; Keil, V.C.; Maumet, C.; Ozsunar, Y.;

et al. Glimr: Cross-border collaborations to promote advanced MRI biomarkers for glioma.

J. Med. Biol. Eng.

2021,

41,

115–125.

[CrossRef] [PubMed]

292. Zhuge, Y.; Ning, H.; Mathen, P.; Cheng, J.Y.; Krauze, A.V.; Camphausen, K.; Miller, R.W. Automated glioma grading on

conventional MRI images using deep convolutional neural networks.

Med. Phys.

2020,

47,

3044–3053. [CrossRef] [PubMed]

293. Wietelmann, D.; Schumacher, M.; Muendel, J. Brain-stem glioma.

Radiologe

1998,

38,

904–912. [CrossRef] [PubMed]

294. Porcari, P.; Hegi, M.E.; Lei, H.; Hamou, M.F.; Vassallo, I.; Capuani, S.; Gruetter, R.; Mlynarik, V. Early detection of human glioma

sphere xenografts in mouse brain using diffusion MRI at 14.1t.

NMR Biomed.

2016,

29,

1577–1589. [CrossRef] [PubMed]

295. Rogers, C.M.; Jones, P.S.; Weinberg, J.S. Intraoperative MRI for brain tumors.

J. Neuro-Oncol.

2021,

151,

479–490. [CrossRef]

[PubMed]

296. Poussaint, T.Y.; Kocak, M.; Vajapeyam, S.; Packer, R.I.; Robertson, R.L.; Geyer, R.; Haas-Kogan, D.; Pollack, I.F.; Vezina, G.;

Zimmerman, R.; et al. MRI as a central component of clinical trials analysis in brainstem glioma: A report from the pediatric

brain tumor consortium (pbtc).

Neuro-Oncology

2011,

13,

417–427. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

43 of 55

297. Cabaj, A.; Bekiesinska-Figatowska, M.; Duczkowska, A.; Duczkowski, M. Brain MRI ﬁndings in neurological complications of

cancer treatment.

Adv. Clin. Exp. Med.

2016,

25,

789–797. [CrossRef] [PubMed]

298. Kjaer, A.; Loft, A.; Law, I.; Berthelsen, A.K.; Borgwardt, L.; Lofgren, J.; Johnbeck, C.B.; Hansen, A.E.; Keller, S.; Holm, S.; et al.

Pet/MRI in cancer patients: First experiences and vision from copenhagen.

Magn. Reson. Mat. Phys. Biol. Med.

2013,

26,

37–47.

[CrossRef]

299. Tsiouris, S.; Bougias, C.; Fotopoulos, A. Principles and current trends in the correlative evaluation of glioma with advanced MRI

techniques and PET.

Hell. J. Nucl. Med.

2019,

22,

206–219.

300. Puttick, S.; Bell, C.; Dowson, N.; Rose, S.; Fay, M. PET, MRI, and simultaneous PET/MRI in the development of diagnostic and

therapeutic strategies for glioma.

Drug Discov. Today

2015,

20,

306–317. [CrossRef] [PubMed]

301. Shankar, A.; Bomanji, J.; Hyare, H. Hybrid PET-MRI imaging in paediatric and TYA brain tumours: Clinical applications and

challenges.

J. Pers. Med.

2020,

10,

218. [CrossRef] [PubMed]

302. Deuschl, C.; Kirchner, J.; Poeppel, T.D.; Schaarschmidt, B.; Kebir, S.; El Hindy, N.; Hense, J.; Quick, H.H.; Glas, M.; Herrmann,

K.; et al. C-11-met PET/MRI for detection of recurrent glioma.

Eur. J. Nucl. Med. Mol. Imaging

2018,

45,

593–601. [CrossRef]

[PubMed]

303. Kebir, S.; Weber, M.; Lazaridis, L.; Deuschl, C.; Schmidt, T.; Monninghoff, C.; Keyvani, K.; Umutlu, L.; Pierscianek, D.; Forsting,

M.; et al. Hybrid c-11-met PET/MRI combined with “machine learning” in glioma diagnosis according to the revised glioma who

classiﬁcation 2016.

Clin. Nucl. Med.

2019,

44,

214–220. [CrossRef]

304. Suh, C.H.; Kim, H.S.; Jung, S.C.; Choi, C.G.; Kim, S.J. Perfusion MRI as a diagnostic biomarker for differentiating glioma from

brain metastasis: A systematic review and meta-analysis.

Eur. Radiol.

2018,

28,

3819–3831. [CrossRef]

305. Xu, X.; Chan, K.W.Y.; Knutsson, L.; Artemov, D.; Xu, J.D.; Liu, G.; Kato, Y.; Lal, B.; Laterra, J.; McMahon, M.T.; et al. Dynamic

glucose enhanced (dge) MRI for combined imaging of blood-brain barrier break down and increased blood volume in brain

cancer.

Magn. Reson. Med.

2015,

74,

1556–1563. [CrossRef]

306. Davis, S.C.; Samkoe, K.S.; O’Hara, J.A.; Gibbs-Strauss, S.L.; Payne, H.L.; Hoopes, P.J.; Paulsen, K.D.; Pogue, B.W. MRI-coupled

ﬂuorescence tomography quantiﬁes EGFR activity in brain tumors.

Acad. Radiol.

2010,

17,

271–276. [CrossRef]

307. Aquino, D.; Gioppo, A.; Finocchiaro, G.; Bruzzone, M.G.; Cuccarini, V. MRI in glioma immunotherapy: Evidence, pitfalls, and

perspectives.

J. Immunol. Res.

2017,

5813951. [CrossRef]

308. Jenkinson, M.D.; Du Plessis, D.G.; Walker, C.; Smith, T.S. Advanced MRI in the management of adult gliomas.

Br. J. Neurosurg.

2007,

21,

550–561. [CrossRef]

309. Jestaedt, L.; Lemke, D.; Weiler, M.; Pfenning, P.N.; Heiland, S.; Wick, W.; Bendszus, M. Gadoﬂuorine m enhanced MRI in

experimental glioma: Superior and persistent intracellular tumor enhancement compared with conventional MRI.

J. Magn. Reson.

Imaging

2012,

35,

551–560. [CrossRef]

310. Liao, J.C.; Xia, R.; Liu, T.; Feng, H.; Ai, H.; Song, B.; Gao, F.B. In vivo dynamic monitoring of the biological behavior of labeled c6

glioma by MRI.

Mol. Med. Rep.

2013,

1397–1402. [CrossRef] [PubMed]

311. Zaccagna, F.; Riemer, F.; Priest, A.N.; McLean, M.A.; Allinson, K.; Grist, J.T.; Dragos, C.; Matys, T.; Gillard, J.H.; Watts, C.; et al.

Non-invasive assessment of glioma microstructure using verdict MRI: Correlation with histology.

Eur. Radiol.

2019,

29,

5559–5566.

[CrossRef] [PubMed]

312. Anka, A.; Thompson, P.; Mott, E.; Sharma, R.; Zhang, R.Z.; Cross, N.; Sun, J.Y.; Flask, C.A.; Oleinick, N.L.; Dean, D. Dynamic

contrast enhanced-magnetic resonance imaging (DCE-MRI) for the assessment of pc 4-sensitized photodynamic therapy of a

u87-derived glioma model in the athymic nude rat. In Proceedings of the Conference on Photonic Therapeutics and Diagnostics

VI, San Francisco, CA, USA, 23–25 January 2010; SPIE—Society of Photo-Optical Instrumentation Engineers: San Francisco, CA,

USA, 2010.

313. Belle, V.; Anka, A.; Cross, N.; Thompson, P.; Mott, E.; Sharma, R.; Gray, K.; Zhang, R.Z.; Xu, Y.S.; Sun, J.Y.; et al. Dynamic contrast

enhanced-magnetic resonance imaging (DCE-MRI) of photodynamic therapy (PDT) outcome and associated changes in the

blood-brain barrier following PC 4-PDT of glioma in an athymic nude rat model. In Proceedings of the Conference on Photonic

Therapeutics and Diagnostics VIII, San Francisco, CA, USA, 21–24 January 2012; SPIE-Society of Photo-Optical Instrumentation

Engineers: San Francisco, CA, USA, 2012.

314. Young, J.R.; Ressler, J.A.; Mortimer, J.E.; Schmolze, D.; Fitzgibbons, M.; Chen, B.H.T. Performance of enhancement on brain MRI

for identifying her2 overexpression in breast cancer brain metastases.

Eur. J. Radiol.

2021,

144,

6. [CrossRef]

315. Papadimitrakopoulou, V.A. Carcinogenesis of head and neck cancer and the role of chemoprevention in its reversal.

Curr. Opin.

Oncol.

2000,

12,

240–245. [CrossRef]

316. Loeffelbein, D.J.; Souvatzoglou, M.; Wankerl, V.; Dinges, J.; Ritschl, L.M.; Mucke, T.; Pickhard, A.; Eiber, M.; Schwaiger, M.; Beer,

A.J. Diagnostic value of retrospective PET-MRI fusion in head-and-neck cancer.

BMC Cancer

2014,

14,

10. [CrossRef] [PubMed]

317. Wang, K.; Mullins, B.T.; Falchook, A.D.; Lian, J.; He, K.L.; Shen, D.G.; Dance, M.; Lin, W.L.; Sills, T.M.; Das, S.K.; et al. Evaluation

of PET/MRI for tumor volume delineation for head and neck cancer.

Front. Oncol.

2017,

8. [CrossRef] [PubMed]

318. Grosse, J.; Hellwig, D. Pet/CT and PET/MRI in head and neck cancer.

Laryngorhinootologie

2020,

99,

12–21.

319. Hermans, R. Diffusion-weighted MRI in head and neck cancer.

Curr. Opin. Otolaryngol. Head Neck Surg.

2010,

18,

72–78. [CrossRef]

320. Thoeny, H.C. Diffusion-weighted MRI in head and neck radiology: Applications in oncology.

Cancer Imaging

2011,

10,

209–214.

[CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

44 of 55

321. Schakel, T.; Hoogduin, J.M.; Terhaard, C.H.J.; Philippens, M.E.P. Diffusion weighted MRI in head-and-neck cancer: Geometrical

accuracy.

Radiother. Oncol.

2013,

109,

394–397. [CrossRef]

322. El Beltagi, A.H.; Elsotouhy, A.H.; Own, A.M.; Abdelfattah, W.; Nair, K.; Vattoth, S. Functional magnetic resonance imaging of

head and neck cancer: Performance and potential.

Neuradiol. J.

2019,

32,

36–52. [CrossRef] [PubMed]

323. Zhao, B.X.; Soraghan, J.; Di-caterina, G.; Grose, D.; Doshi, T. Automatic 3D detection and segmentation of head and neck cancer

from MRI data. In Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland,

26–28 November 2018.

324. Chen, A.M.; Hsu, S.; Lamb, J.; Yang, Y.; Agazaryan, N.; Steinberg, M.L.; Low, D.A.; Cao, M. MRI-guided radiotherapy for head

and neck cancer: Initial clinical experience.

Clin. Transl. Oncol.

2018,

20,

160–168. [CrossRef] [PubMed]

325. Baulch, J.; Gandhi, M.; Sommerville, J.; Panizza, B. 3t MRI evaluation of large nerve perineural spread of head and neck cancers.

Med. Imaging Radiat. Oncol.

2015,

59,

578–585. [CrossRef] [PubMed]

326. Kanda, T.; Kitajima, K.; Suenaga, Y.; Konishi, J.; Sasaki, R.; Morimoto, K.; Saito, M.; Otsuki, N.; Nibu, K.; Sugimura, K. Value of

retrospective image fusion of

F-FDG PET and MRI for preoperative staging of head and neck cancer: Comparison with PET/CT

and contrast-enhanced neck MRI.

Eur. J. Radiol.

2013,

82,

2005–2010. [CrossRef] [PubMed]

327. Baghi, M.; Mack, M.G.; Hambek, M.; Rieger, J.; Vogl, T.; Gstoettner, W.; Knecht, R. The efﬁcacy of MRI with ultrasmall

superparamagnetic iron oxide particles (uspio) in head and neck cancers.

Anticancer Res.

2005,

25,

3665–3670. [PubMed]

328. Rahbari, R.; Zhang, L.S.; Kebebew, E. Thyroid cancer gender disparity.

Future Oncol.

2010,

1771–1779. [CrossRef] [PubMed]

329. Dinauer, C.A.; Breuer, C.; Rivkees, S.A. Differentiated thyroid cancer in children: Diagnosis and management.

Curr. Opin. Oncol.

2008,

20,

59–65. [CrossRef]

330. Massimino, M.; Evans, D.B.; Podda, M.; Spinelli, C.; Collini, P.; Pizzi, N.; Bleyer, A. Thyroid cancer in adolescents and young

adults.

Pediatr. Blood Cancer

2018,

65,

9. [CrossRef]

331. Regalbuto, C.; Frasca, F.; Pellegriti, G.; Malandrino, P.; Marturano, I.; Di Carlo, I.; Pezzino, V. Update on thyroid cancer treatment.

Future Oncol.

2012,

1331–1348. [CrossRef]

332. CynaGorse, F.; Toubert, M.E.; Zagdanski, A.M.; deKerviler, E.; Feger, C.; Benchaib, N.; Attal, P.; Frija, J.; LavalJeantet, M.

Recurrence of differentiated thyroid carcinomas: Value of MRI.

J. Radiol.

1996,

77,

1195–1200.

333. Chen, Q.H.; Raghavan, P.; Mukherjee, S.; Jameson, M.J.; Patrie, J.; Xin, W.J.; Xian, J.F.; Wang, Z.C.; Levine, P.A.; Wintermark, M.

Accuracy of MRI for the diagnosis of metastatic cervical lymphadenopathy in patients with thyroid cancer.

Radiol. Med.

2015,

120,

959–966. [CrossRef] [PubMed]

334. Gross, N.D.; Weissman, J.L.; Talbot, J.M.; Andersen, P.E.; Wax, M.K.; Cohen, J.I. MRI detection of cervical metastasis from

differentiated thyroid carcinoma.

Laryngoscope

2001,

111,

1905–1909. [CrossRef] [PubMed]

335. Liu, Z.M.; Xun, X.Q.; Wang, Y.Z.; Mei, L.; He, L.; Zeng, W.; Wang, C.Y.; Tao, H. MRI and ultrasonography detection of cervical

lymph node metastases in differentiated thyroid carcinoma before reoperation.

Am. J. Transl. Res.

2014,

147–154. [PubMed]

336. Samanci, C.; Onal, Y.; Sager, S.; Asa, S.; Ustabasioglu, F.E.; Alis, D.; Akman, C.; Sonmezoglu, K. Diagnostic capabilities of MRI

versus

F FDG PET-ct in postoperative patients with thyroglobulin positive,

131

I-negative local recurrent or metastatic thyroid

cancer.

Curr. Med. Imaging

2019,

15,

956–964. [CrossRef]

337. Wang, J.C.; Takashima, S.; Matsushita, T.; Takayama, F.; Kobayashi, T.; Kadoya, M. Esophageal invasion by thyroid carcinomas:

Prediction using magnetic resonance imaging.

J. Comput. Assist. Tomogr.

2003,

27,

18–25. [CrossRef]

338. Brown, A.M.; Nagala, S.; McLean, M.A.; Lu, Y.G.; Scofﬁngs, D.; Apte, A.; Gonen, M.; Stambuk, H.E.; Shaha, A.R.; Tuttle, R.M.;

et al. Multi-institutional validation of a novel textural analysis tool for preoperative stratiﬁcation of suspected thyroid tumors on

diffusion-weighted MRI.

Magn. Reson. Med.

2016,

75,

1708–1716. [CrossRef]

339. Naglah, A.; Khalifa, F.; Khaled, R.; Razek, A.; Ghazal, M.; Giridharan, G.; El-Baz, A. Novel MRI-based cad system for early

detection of thyroid cancer using multi-input CNN.

Sensors

2021,

21,

3878. [CrossRef]

340. Abd el Aziz, L.M.; Hamisa, M.; Badwy, M.E. Differentiation of thyroid nodules using diffusion-weighted MRI.

Alex. J. Med.

2015,

51,

305–309. [CrossRef]

341. Taha, M.S.; Hassan, O.; Amir, M.; Taha, T.; Riad, M.A. Diffusion-weighted MRI in diagnosing thyroid cartilage invasion in

laryngeal carcinoma.

Eur. Arch. Oto-Rhino-Laryngol.

2014,

271,

2511–2516. [CrossRef]

342. Binse, I.; Poeppel, T.D.; Ruhlmann, M.; Gomez, B.; Umutlu, L.; Bockisch, A.; Rosenbaum-Krumme, S.J. Imaging with i-124

in differentiated thyroid carcinoma: Is PET/MRI superior to PET/CT?

Eur. J. Nucl. Med. Mol. Imaging

2016,

43,

1011–1017.

[CrossRef]

343. Seiboth, L.; Van Nostrand, D.; Wartofsky, L.; Ousman, Y.; Jonklaas, J.; Butler, C.; Atkins, F.; Burman, K. Utility of PET/neck MRI

digital fusion images in the management of recurrent or persistent thyroid cancer.

Thyroid

2008,

18,

103–111. [CrossRef] [PubMed]

344. Teller, P.; Jefford, V.J.; Gabram, S.G.A.; Newell, M.; Carlson, G.W. The utility of breast MRI in the management of breast cancer.

Breast J.

2010,

16,

394–403. [CrossRef] [PubMed]

345. Mann, R.M.; Cho, N.; Moy, L. Breast MRI: State of the art.

Radiology

2019,

292,

520–536. [CrossRef] [PubMed]

346. Militello, C.; Rundo, L.; Dimarco, M.; Orlando, A.; Conti, V.; Woitek, R.; D’Angelo, I.; Bartolotta, T.V.; Russo, G. Semi-automated

and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering.

Biomed. Signal

Process. Control

2022,

71,

12. [CrossRef]

347. Vogel, W.V.; Nestle, U.; Valli, M.C. Pet/MRI in breast cancer.

Clin. Transl. Imaging

2017,

71–78. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

45 of 55

348. Heacock, L.; Reig, B.; Lewin, A.A.; Toth, H.K.; Moy, L.; Lee, C.S. Abbreviated breast MRI: Road to clinical implementation.

Breast Imaging

2020,

201–214. [CrossRef]

349. Mann, R.M.; Kuhl, C.K.; Moy, L. Contrast-enhanced MRI for breast cancer screening.

J. Magn. Reson. Imaging

2019,

50,

377–390.

[CrossRef]

350. van Zandwijk, N. Chemoprevention of lung cancer.

Lung Cancer

2001,

34,

S91–S94. [CrossRef]

351. Hintze, C.; Dinkel, J.; Biederer, J.; Heussel, C.P.; Puderbach, M. New procedures—Comprehensive staging of lung cancer with

MRI.

Radiologe

2010,

50,

699–705. [CrossRef]

352. Khalil, A.; Bouhela, T.; Carette, M.F. Contribution of MRI in lung cancer staging.

JBR-BTR

2013,

96,

132–141. [CrossRef]

353. Biederer, J.; Ohno, Y.; Hatabu, H.; Schiebler, M.L.; van Beek, E.J.R.; Vogel-Claussen, J.; Kauczor, H.U. Screening for lung cancer:

Does MRI have a role?

Eur. J. Radiol.

2017,

86,

353–360. [CrossRef]

354. Meier-Schroers, M.; Homsi, R.; Schild, H.H.; Thomas, D. Lung cancer screening with MRI: Characterization of nodules with

different non-enhanced MRI sequences.

Acta Radiol.

2019,

60,

168–176. [CrossRef]

355. Meier-Schroers, M.; Homsi, R.; Skowasch, D.; Buermann, J.; Zipfel, M.; Schild, H.H.; Thomas, D. Lung cancer screening with MRI:

Results of the ﬁrst screening round.

J. Cancer Res. Clin. Oncol.

2018,

144,

117–125. [CrossRef]

356. Cobben, D.C.P.; de Boer, H.C.J.; Tijssen, R.H.; Rutten, E.; van Vulpen, M.; Peerlings, J.; Troost, E.G.C.; Hoffmann, A.L.; van Lier, A.

Emerging role of MRI for radiation treatment planning in lung cancer.

Technol. Cancer Res. Treat.

2016,

15,

NP47–NP60. [CrossRef]

357. Weller, A.; Papoutsaki, M.V.; Waterton, J.C.; Chiti, A.; Stroobants, S.; Kuijer, J.; Blackledge, M.; Morgan, V.; deSouza, N.M.

Diffusion-weighted (dw) MRI in lung cancers: Adc test-retest repeatability.

Eur. Radiol.

2017,

27,

4552–4562. [CrossRef]

358. Kim, H.S.; Lee, K.S.; Ohno, Y.; van Beek, E.J.R.; Biederer, J.; Int Workshop Pulm, F. PET/CT versus MRI for diagnosis, staging, and

follow-up of lung cancer.

J. Magn. Reson. Imaging

2015,

42,

247–260. [CrossRef]

359. Ohno, Y.; Koyama, H.; Lee, H.Y.; Yoshikawa, T.; Sugimura, K. Magnetic resonance imaging (MRI) and positron emission

tomography (PET)/MRI for lung cancer staging.

J. Thorac. Imaging

2016,

31,

215–227. [CrossRef]

360. Pasechnikov, V.; Chukov, S.; Fedorov, E.; Kikuste, I.; Leja, M. Gastric cancer: Prevention, screening and early diagnosis.

World J.

Gastroenterol.

2014,

20,

13842–13862. [CrossRef]

361. Renzulli, M.; Clemente, A.; Spinelli, D.; Ierardi, A.M.; Marasco, G.; Farina, D.; Brocchi, S.; Ravaioli, M.; Pettinari, I.; Cescon, M.;

et al. Gastric cancer staging: Is it time for magnetic resonance imaging?

Cancers

2020,

12,

1402. [CrossRef]

362. Zhang, Y.J.; Yu, J.C. The role of MRI in the diagnosis and treatment of gastric cancer.

Diagn. Interv. Radiol.

2020,

26,

176–182.

[CrossRef]

363. De Vuysere, S.; Vandecaveye, V.; De Bruecker, Y.; Carton, S.; Vermeiren, K.; Tollens, T.; De Keyzer, F.; Dresen, R.C. Accuracy of

whole-body diffusion-weighted MRI (wb-dwi/MRI) in diagnosis, staging and follow-up of gastric cancer, in comparison to CT: A

pilot study.

BMC Med. Imaging

2021,

21,

9. [CrossRef]

364. Caivano, R.; Rabasco, P.; Lotumolo, A.; D’ Antuono, F.; Zandolino, A.; Villonio, A.; Macarini, L.; Guglielmi, G.; Salvatore, M.;

Cammarota, A. Gastric cancer: The role of diffusion weighted imaging in the preoperative staging.

Cancer Investig.

2014,

32,

184–190. [CrossRef]

365. Hasbahceci, M.; Akcakaya, A.; Memmi, N.; Turkmen, I.; Cipe, G.; Yildiz, P.; Arici, D.S.; Muslumanoglu, M. Diffusion MRI on

lymph node staging of gastric adenocarcinoma.

Quant. Imaging Med. Surg.

2015,

392–400.

366. Takahashi, H.; Yano, H.; Matsushita, M.; Monden, T.; Kinuta, M.; Tateishi, H.; Nakano, Y.; Matsui, S.; Iwazawa, T.; Kanoh, T.; et al.

Preoperative staging of gastric cancer: Diagnosing the depth of invasion in the gastric wall with MRI. In Proceedings of the 3rd

International Gastric Cancer Congress, Seoul, Korea, 27–30 April 1999; pp. 411–414.

367. Tokuhara, T.; Tanigawa, N.; Matsuki, M.; Nomura, E.; Mabuchi, H.; Lee, S.W.; Tatsumi, Y.; Nishimura, H.; Yoshinaka, R.;

Kurisu, Y.; et al. Evaluation of lymph node metastases in gastric cancer using magnetic resonance imaging with ultrasmall

superparamagnetic iron oxide (uspio): Diagnostic performance in post-contrast images using new diagnostic criteria.

Gastric

Cancer

2008,

11,

194–200. [CrossRef]

368. Arocena, M.G.; Barturen, A.; Bujanda, L.; Casado, O.; Ramirez, M.M.; Oleagoitia, J.M.; Iturri, M.G.; Mugica, P.; Cosme, A.;

Gutierrez-Stampa, M.A.; et al. MRI and endoscopic ultrasonography in the staging of gastric cancer.

Rev. Esp. Enferm. Dig.

2006,

98,

582–586. [CrossRef]

369. Malaj, A.; Bilaj, F.; Shahini, A.; Miraka, M. Ct/MRI accuracy in detecting and determining preoperative stage of gastric

adenocarcinoma in albania.

Wspolczesna Onkol.

2017,

21,

168–173. [CrossRef]

370. Heye, T.; Kuntz, C.; Dux, M.; Encke, J.; Palmowski, M.; Autschbach, F.; Volke, F.; Kauffmann, G.W.; Grenacher, L. Ct and

endoscopic ultrasound in comparison to endoluminal MRI-preliminary results in staging gastric carcinoma.

Eur. J. Radiol.

2009,

70,

336–341. [CrossRef]

371. Kim, A.Y.; Han, J.K.; Seong, C.K.; Kim, T.K.; Choi, B.I. MRI in staging advanced gastric cancer: Is it useful compared with spiral

CT?

J. Comput. Assist. Tomogr.

2000,

24,

389–394. [CrossRef]

372. Maccioni, F.; Marcelli, G.; Al Ansari, N.; Zippi, M.; De Marco, V.; Kagarmanova, A.; Vestri, A.; Marcheg-giano-Clarke, L.; Marini,

M. Preoperative t and n staging of gastric cancer: Magnetic resonance imaging (MRI) versus multi detector computed tomography

(MDCT).

Clin. Ter.

2010,

161,

e57–e62.

373. Ma, L.; Xu, X.W.; Zhang, M.; Zheng, S.Q.; Zhang, B.; Zhang, W.; Wang, P.J. Dynamic contrast-enhanced MRI of gastric cancer:

Correlations of the pharmacokinetic parameters with histological type, lauren classiﬁcation, and angiogenesis.

Magn. Reson.

Imaging

2017,

37,

27–32. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

46 of 55

374. Joo, I.; Lee, J.M.; Han, J.K.; Yang, H.K.; Lee, H.J.; Choi, B.I. Dynamic contrast-enhanced MRI of gastric cancer: Correlation of the

perfusion parameters with pathological prognostic factors.

J. Magn. Reson. Imaging

2015,

41,

1608–1614. [CrossRef]

375. Bae, S.W.; Berlth, F.; Jeong, K.Y.; Suh, Y.S.; Kong, S.H.; Lee, H.J.; Kim, W.H.; Chung, J.K.; Yang, H.K. Establishment of a

FDG-PET/MRI imaging protocol for gastric cancer pdx as a preclinical research tool.

J. Gasric Cancer

2020,

20,

60–71. [CrossRef]

376. Lee, D.H.; Kim, S.H.; Joo, I.; Hur, B.Y.; Han, J.K. Comparison between

F-FDG PET/MRI and mdct for the assessment of

preoperative staging and resectability of gastric cancer.

Eur. J. Radiol.

2016,

85,

1085–1091. [CrossRef]

377. Liu, Y.; Zheng, D.; Liu, J.J.; Cui, J.X.; Xi, H.Q.; Zhang, K.C.; Huang, X.H.; Wei, B.; Wang, X.X.; Xu, B.X.; et al. Comparing PET/MRI

with PET/CT for pretreatment staging of gastric cancer.

Gastroenterol. Res. Pract.

2019,

9564627. [CrossRef]

378. Yuan, Y.; Chen, L.G.; Ren, S.N.; Wang, Z.; Chen, Y.K.; Jin, A.G.; Li, S.; Fang, X.; Wang, T.G.; Bian, Y.; et al. Diagnostic performance

in t staging for patients with esophagogastric junction cancer using high-resolution MRI: A comparison with conventional MRI at

3 tesla.

Cancer Imaging

2019,

19,

9. [CrossRef]

379. Pang, L.; Wang, J.; Fan, Y.; Xu, R.; Bai, Y.P.; Bai, L.C. Correlations of tnm staging and lymph node metastasis of gastric cancer with

MRI features and vegf expression.

Cancer Biomark.

2018,

23,

53–59. [CrossRef]

380. Giganti, F.; Orsenigo, E.; Arcidiacono, P.G.; Nicoletti, R.; Albarello, L.; Ambrosi, A.; Salerno, A.; Esposito, A.; Petrone, M.C.;

Chiari, D.; et al. Preoperative locoregional staging of gastric cancer: Is there a place for magnetic resonance imaging? Prospective

comparison with eus and multidetector computed tomography.

Gastric Cancer

2016,

19,

216–225. [CrossRef]

381. Garrido-Laguna, I.; Hidalgo, M. Pancreatic cancer: From state-of-the-art treatments to promising novel therapies.

Nat. Rev. Clin.

Oncol.

2015,

12,

319–334. [CrossRef]

382. Barnes, C.A.; Krzywda, E.; Lahiff, S.; McDowell, D.; Christians, K.K.; Knechtges, P.; Tolat, P.; Hohenwalter, M.; Dua, K.; Khan,

A.H.; et al. Development of a high risk pancreatic screening clinic using 3.0 t MRI.

Fam. Cancer

2018,

17,

101–111. [CrossRef]

383. Gaa, J.; Fingerle, A.A.; Holzapfel, K.; Rummeny, E.J. MRI for malignant pancreatic tumors.

Radiologe

2009,

49,

124–130. [CrossRef]

384. Ueno, M.; Niwa, T.; Ohkawa, S.; Amano, A.; Masaki, T.; Miyakawa, K.; Yoshida, T. The usefulness of perfusion-weighted magnetic

resonance imaging in advanced pancreatic cancer.

Pancreas

2009,

38,

644–648. [CrossRef] [PubMed]

385. Gassert, F.G.; Ziegelmayer, S.; Luitjens, J.; Gassert, F.T.; Tollens, F.; Rink, J.; Makowski, M.R.; Rubenthaler, J.; Froelich, M.F.

Additional MRI for initial m-staging in pancreatic cancer: A cost-effectiveness analysis.

Eur. Radiol.

2021,

1–9. [CrossRef]

[PubMed]

386. Visser, B.C.; Yeh, B.M.; Qayyum, A.; Way, L.W.; McCulloch, C.E.; Coakley, F.V. Characterization of cystic pancreatic masses:

Relative accuracy of CT and MRI.

Am. J. Roentgenol.

2007,

189,

648–656. [CrossRef] [PubMed]

387. Chen, F.M.; Ni, J.M.; Zhang, Z.Y.; Zhang, L.; Li, B.; Jiang, C.J. Presurgical evaluation of pancreatic cancer: A comprehensive

imaging comparison of CT versus MRI.

Am. J. Roentgenol.

2016,

206,

526–535. [CrossRef] [PubMed]

388. Deng, Y.; Ming, B.; Wu, J.L.; Zhou, T.; Zhang, S.Y.; Chen, Y.; Lan, C.; Zhang, X.M. Magnetic resonance imaging for preoperative

staging of pancreatic cancer based on the 8th edition of ajcc guidelines.

J. Gastrointest. Oncol.

2020,

11,

329–336. [CrossRef]

[PubMed]

389. Litjens, G.; Riviere, D.M.; van Geenen, E.J.M.; Radema, S.A.; Brosens, L.A.A.; Prokop, M.; van Laarhoven, C.; Hermans, J.J.

Diagnostic accuracy of contrast-enhanced diffusion-weighted MRI for liver metastases of pancreatic cancer: Towards adequate

staging and follow-up of pancreatic cancer—DIA-PANC study: Study protocol for an international, multicenter, diagnostic trial.

BMC Cancer

2020,

20,

8. [CrossRef]

390. Jhaveri, K.S.; Jandaghi, A.B.; Thipphavong, S.; Espin-Garcia, O.; Dodd, A.; Hutchinson, S.; Reichman, T.W.; Moulton, C.A.;

McGilvary, I.D.; Gallinger, S. Can preoperative liver MRI with gadoxetic acid help reduce open-close laparotomies for curative

intent pancreatic cancer surgery?

Cancer Imaging

2021,

21,

12. [CrossRef]

391. Riviere, D.M.; van Geenen, E.J.M.; van der Kolk, B.M.; Nagtegaal, I.D.; Radema, S.A.; van Laarhoven, C.; Hermans, J.J. Improving

preoperative detection of synchronous liver metastases in pancreatic cancer with combined contrast-enhanced and diffusion-

weighted MRI.

Abdom. Radiol.

2019,

44,

1756–1765. [CrossRef]

392. Yang, M.; Hopp, A.C.; Bekaii-Saab, T.S.; Collins, J.M. Sister mary joseph nodule in advanced pancreatic adenocarcinoma identiﬁed

F-FDG PET/MRI.

J. Nucl. Med. Technol.

2019,

47,

341–342. [CrossRef]

393. Sandrasegaran, K.; Nutakki, K.; Tahir, B.; Dhanabal, A.; Tann, M.; Cote, G.A. Use of diffusion-weighted MRI to differentiate

chronic pancreatitis from pancreatic cancer.

Am. J. Roentgenol.

2013,

201,

1002–1008. [CrossRef]

394. Kim, J.K.; Altun, E.; Elias, J.; Pamuklar, E.; Rivero, H.; Semelka, R.C. Focal pancreatic mass: Distinction of pancreatic cancer from

chronic pancreatitis using gadolinium-enhanced 3d-gradient-echo MRI.

J. Magn. Reson. Imaging

2007,

26,

313–322. [CrossRef]

[PubMed]

395. Liu, S.A.; Fu, W.W.; Liu, Z.J.; Liu, M.; Ren, R.M.; Zhai, H.X.; Li, C.L. MRI-guided celiac plexus neurolysis for pancreatic cancer

pain: Efﬁcacy and safety.

J. Magn. Reson. Imaging

2016,

44,

923–928. [CrossRef] [PubMed]

396. Niwa, T.; Ueno, M.; Shinya, N.; Gotoh, T.; Kwee, T.C.; Takahara, T.; Yoshida, T.; Ohkawa, S.; Doiuchi, T.; Inoue, T. Dynamic

susceptibility contrast MRI in advanced pancreatic cancer: Semi-automated analysis to predict response to chemotherapy.

NMR

Biomed.

2010,

23,

347–352. [CrossRef] [PubMed]

397. Zhang, T.; Zhang, F.; Meng, Y.F.; Wang, H.; Le, T.; Wei, B.J.; Lee, D.; Willis, P.; Shen, B.Z.; Yang, X.M. Diffusion-weighted MRI

monitoring of pancreatic cancer response to radiofrequency heat-enhanced intratumor chemotherapy.

NMR Biomed.

2013,

26,

1762–1767. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

47 of 55

398. Grandhi, M.S.; Kim, A.K.; Ronnekleiv-Kelly, S.M.; Kamel, I.R.; Ghasebeh, M.A.; Pawlik, T.M. Hepatocellular carcinoma: From

diagnosis to treatment.

Surg. Oncol.

2016,

25,

74–85. [CrossRef]

399. Wang, G.B.; Zhu, S.C.; Li, X.K. Comparison of values of CT and MRI imaging in the diagnosis of hepatocellular carcinoma and

analysis of prognostic factors.

Oncol. Lett.

2019,

17,

1184–1188. [CrossRef]

400. Kloeckner, R.; dos Santos, D.P.; Kreitner, K.F.; Leicher-Duber, A.; Weinmann, A.; Mittler, J.; Duber, C. Quantitative assessment of

washout in hepatocellular carcinoma using MRI.

BMC Cancer

2016,

16,

8. [CrossRef]

401. Sanghvi, T.; Boyum, J.; Spilseth, B.; Schat, R.; Estby, H.; Taylor, A. MRI for hepatocellular carcinoma: A primer for magnetic

resonance imaging interpretation.

Abdom. Radiol.

2018,

43,

1143–1151. [CrossRef]

402. Hectors, S.J.; Lewis, S.; Besa, C.; King, M.J.; Said, D.; Putra, J.; Ward, S.; Higashi, T.; Thung, S.; Yao, S.; et al. MRI radiomics

features predict immuno-oncological characteristics of hepatocellular carcinoma.

Eur. Radiol.

2020,

30,

3759–3769. [CrossRef]

403. Low, H.M.; Choi, J.Y.; Tan, C.H. Pathological variants of hepatocellular carcinoma on MRI: Emphasis on histopathologic

correlation.

Abdom. Radiol.

2019,

44,

493–508. [CrossRef]

404. Kim, Y.N.; Song, J.S.; Moon, W.S.; Hwang, H.P.; Kim, Y.K. Intra-individual comparison of hepatocellular carcinoma imaging

computed tomconstrast-enhancedtte computed tomography, gadopentetate dimeglumine-enhanced MRI, and gadoxetic acid-

enhanced MRI.

Acta Radiol.

2018,

59,

639–648. [CrossRef] [PubMed]

405. Gabr, A.E.; Mikhael, H.S.W.; El-Maadawy, S.M. Comparison between subtraction and dynamic MRI in assessing treatment

response following radiofrequency ablation in patients with hepatocellular carcinoma.

Egypt. J. Radiol. Nucl. Med.

2021,

52,

10.

[CrossRef]

406. Jha, R.C.; Zanello, P.A.; Nguyen, X.M.; Pehlivanova, M.; Johnson, L.B.; Fishbein, T.; Shetty, K. Small hepatocellular carcinoma:

MRI ﬁndings for predicting tumor growth rates.

Acad. Radiol.

2014,

21,

1455–1464. [PubMed]

407. Abdullah, S.S.; Pialat, J.B.; Wiart, M.; Duboeuf, F.; Mabrut, J.Y.; Bancel, B.; Rode, A.; Ducerf, C.; Baulieux, J.; Berthezene, Y.

Characterization of hepatocellular carcinoma and colorectal liver metastasis by means of perfusion MRI.

J. Magn. Reson. Imaging

2008,

28,

390–395. [CrossRef]

408. Gong, X.Q.; Tao, Y.Y.; Wu, Y.K.; Liu, N.; Yu, X.; Wang, R.; Zheng, J.; Liu, N.; Huang, X.H.; Li, J.D.; et al. Progress of MRI radiomics

in hepatocellular carcinoma.

Front. Oncol.

2021,

11,

13. [CrossRef]

409. Carbonell, G.; Kennedy, P.; Bane, O.; Kirmani, A.; El Homsi, M.; Stocker, D.; Said, D.; Mukherjee, P.; Gevaert, O.; Lewis, S.; et al.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma.

Eur. Radiol.

2021,

1–11. [CrossRef]

410. Kim, H.; Park, M.S.; Choi, J.Y.; Park, Y.N.; Kim, M.J.; Kim, K.S.; Choi, J.S.; Han, K.H.; Kim, E.; Kim, K.W. Can microvessel invasion

of hepatocellular carcinoma be predicted by pre-operative MRI?

Eur. Radiol.

2009,

19,

1744–1751. [CrossRef]

411. Hennedige, T.; Venkatesh, S.K. Imaging of hepatocellular carcinoma: Diagnosis, staging and treatment monitoring.

Cancer Imaging

2013,

12,

530–547. [CrossRef]

412. Taneja, S.; Taneja, R.; Kashyap, V.; Jha, A.; Jena, A. Ga-68-psma uptake in hepatocellular carcinoma.

Clin. Nucl. Med.

2017,

42,

E69–E70. [CrossRef]

413. Dondi, F.; Albano, D.; Cerudelli, E.; Gazzilli, M.; Giubbini, R.; Treglia, G.; Bertagna, F. Radiolabelled psma PET/CT or PET/MRI

in hepatocellular carcinoma (HCC): A systematic review.

Clin. Transl. Imaging

2020,

461–467. [CrossRef]

414. Qiu, Z.L.; Pan, Y.S.; Wei, J.; Wu, D.J.; Xia, Y.; Shen, D.G. Predicting symptoms from multiphasic MRI via multi-instance attention

learning for hepatocellular carcinoma grading. In Proceedings of the International Conference on Medical Image Computing and

Computer Assisted Intervention (MICCAI), Strasbourg, France, 27 September–1 October 2021; Springer: Cham, Switzerland,

2021; pp. 439–448.

415. Saito, K.; Ledsam, J.; Sugimoto, K.; Sourbron, S.; Araki, Y.; Tokuuye, K. Dce-MRI for early prediction of response in hepatocellular

carcinoma after tace and sorafenib therapy: A pilot study.

J. Belg. Soc. Radiol.

2018,

102,

9. [CrossRef] [PubMed]

416. Fan, Y.F.; Yu, Y.X.; Wang, X.M.; Hu, M.J.; Du, M.Z.; Guo, L.C.A.; Sun, S.F.; Hu, C.H. Texture analysis based on gd-eob-dtpa-

enhanced MRI for identifying vessels encapsulating tumor clusters (vetc)-positive hepatocellular carcinoma.

J. Hepatocell.

Carcinoma

2021,

349–359. [CrossRef] [PubMed]

417. Li, X.Q.; Wang, X.; Zhao, D.W.; Sun, J.; Liu, J.J.; Lin, D.D.; Yang, G.; Liu, H.; Xia, Z.Y.; Jia, C.Y.; et al. Application of gd-eob-

dtpa-enhanced magnetic resonance imaging (MRI) in hepatocellular carcinoma.

World J. Surg. Oncol.

2020,

18,

219. [CrossRef]

[PubMed]

418. Hanna, R.F.; Kased, N.; Kwan, S.W.; Gamst, A.C.; Santosa, A.C.; Hassanein, T.; Sirlin, C.B. Double-contrast MRI for accurate

staging of hepatocellular carcinoma in patients with cirrhosis.

Am. J. Roentgenol.

2008,

190,

47–57. [CrossRef]

419. Wu, Y.N.; Huang, L.N.; Li, B.S.; Li, H.G. The diagnostic value of gd-eob-dtpa-enhanced MRI scans in small hepatocellular

carcinoma in patients with liver cirrhosis.

Int. J. Clin. Exp. Med.

2020,

13,

6909–6915.

420. Taouli, B.; Johnson, R.S.; Hajdu, C.H.; Oei, M.T.H.; Merad, M.; Yee, H.; Rusinek, H. Hepatocellular carcinoma: Perfusion

quantiﬁcation with dynamic contrast-enhanced MRI.

Am. J. Roentgenol.

2013,

201,

795–800. [CrossRef]

421. Akhtar, S.; Hussain, M.; Ali, S.; Maqsood, S.; Akram, S.; Abbas, N. Comparison of positive predictive value of multiphasic

dynamic contrast enhanced MRI with dynamic contrast enhanced ct for the detection of hepatocellular carcinoma.

Pak. J. Med

Health Sci.

2020,

14,

562–564.

422. Gluskin, J.S.; Chegai, F.; Monti, S.; Squillaci, E.; Mannelli, L. Hepatocellular carcinoma and diffusion-weighted MRI: Detection

and evaluation of treatment response.

J. Cancer

2016,

1565–1570. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

48 of 55

423. Chen, H.Y.; Hou, Y.L.; Ma, X.M.; Xie, H.Y.; Ye, M.; Bai, Y.R. Ct and MRI in target delineation in primary hepatocellular carcinoma.

Cancer Radiother.

2013,

17,

750–754. [CrossRef]

424. Zemour, J.; Marty, M.; Lapuyade, B.; Collet, D.; Chiche, L. Gallbladder tumor and pseudotumor: Diagnosis and management.

Visc. Surg.

2014,

151,

289–300. [CrossRef]

425. Tseng, J.H.; Wan, Y.L.; Hung, C.F.; Ng, K.K.; Pan, K.T.; Chou, A.S.B.; Liu, N.J. Diagnosis and staging of gallbladder carcinoma—

Evaluation with dynamic MR imaging.

Clin. Imaging

2002,

26,

177–182. [CrossRef]

426. Vendrami, C.L.; Magnetta, M.J.; Mittal, P.K.; Moreno, C.C.; Miller, F.H. Gallbladder carcinoma and its differential diagnosis at

MRI: What radiologists should know.

Radiographics

2021,

41,

78–95. [CrossRef] [PubMed]

427. Wu, S.S.; Zou, X.L.; Wang, Q.X.; Hu, D.Y.; Li, Z.; Xu, C.O. Gallbladder carcinoma: An initial clinical experience of reduced

ﬁeld-of-view diffusion-weighted MRI.

Cancer Imaging

2020,

20,

8. [CrossRef] [PubMed]

428. Kuipers, H.; Hoogwater, F.J.H.; Holtman, G.A.; van der Hoorn, A.; de Boer, M.T.; de Haas, R.J. Clinical value of diffusion-weighted

MRI for differentiation between benign and malignant gallbladder disease: A systematic review and meta-analysis.

Acta Radiol.

2021,

62,

987–996. [CrossRef]

429. Corgna, E.; Betti, M.; Gatta, G.; Roila, F.; De Mulder, P.H.M. Renal cancer.

Crit. Rev. Oncol./Hematol.

2007,

64,

247–261. [CrossRef]

430. Bensalah, K.; Albiges, L.; Bernhard, J.C.; Bigot, P.; Bodin, T.; Boissier, R.; Correas, J.M.; Gimel, P.; Hetet, J.F.; Long, J.A.; et al.

French ccafu guidelines—Update 2018–2020: Management of kidney cancer.

Prog. Urol.

2018,

28,

R5–R33. [CrossRef]

431. Couvidat, C.; Eiss, D.; Verkarre, V.; Merran, S.; Correas, J.M.; Mejean, A.; Helenon, O. Renal papillary carcinoma: CT and MRI

features.

Diagn. Interv. Imaging

2014,

95,

1055–1063. [CrossRef]

432. Shinagare, A.B.; Davenport, M.S.; Park, H.; Pedrosa, I.; Remer, E.M.; Chandarana, H.; Doshi, A.M.; Schieda, N.; Smith, A.D.;

Vikram, R.; et al. Lexicon for renal mass terms at CT and MRI: A consensus of the society of abdominal radiology disease-focused

panel on renal cell carcinoma.

Abdom. Radiol.

2021,

46,

703–722. [CrossRef]

433. Vannier, M.W.

Imaging Renal Carcinoma;

Demos Medical Publications: New York, NY, USA, 2011; Volume 2, pp. 37–56.

434. Chiarello, M.A.; Mali, R.D.; Kang, S.K. Diagnostic accuracy of MRI for detection of papillary renal cell carcinoma: A systematic

review and meta-analysis.

Am. J. Roentgenol.

2018,

211,

812–821. [CrossRef]

435. Wehrli, N.E.; Kim, M.J.; Matza, B.W.; Melamed, J.; Taneja, S.S.; Rosenkrantz, A.B. Utility of MRI features in differentiation of

central renal cell carcinoma and renal pelvic urothelial carcinoma.

Am. J. Roentgenol.

2013,

201,

1260–1267. [CrossRef]

436. Palmowski, M.; Schifferdecker, I.; Zwick, S.; Macher-Goeppinger, S.; Laue, H.; Haferkamp, A.; Kauczor, H.U.; Kiessling, F.;

Hallscheidt, P. Tumor perfusion assessed by dynamic contrast-enhanced MRI correlates to the grading of renal cell carcinoma:

Initial results.

Eur. J. Radiol.

2010,

74,

e177–e181. [CrossRef] [PubMed]

437. Lei, Y.; Wang, H.; Li, H.F.; Rao, Y.W.; Liu, J.H.; Tian, S.F.; Ju, Y.; Li, Y.; Chen, A.L.; Chen, L.H.; et al. Diagnostic signiﬁcance of

diffusion-weighted MRI in renal cancer.

BioMed Res. Int.

2015,

172165. [CrossRef] [PubMed]

438. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2016.

CA Cancer J. Clin.

2016,

66,

7–30. [CrossRef]

439. Saito, W.; Amanuma, M.; Tanaka, J.; Heshiki, A. Histopathological analysis of a bladder cancer stalk observed on MRI.

Magn.

Reson. Imaging

2000,

18,

411–415. [CrossRef]

440. Tillou, X.; Grardel, E.; Fourmarier, M.; Bernasconi, T.; Demailly, M.; Hakami, F.; Saint, F.; Petit, J. Can MRI be used to distinguish

between superﬁcial and invasive transitional cell bladder cancer?

Prog. Urol.

2008,

18,

440–444. [CrossRef]

441. Dolz, J.; Xu, X.P.; Rony, J.; Yuan, J.; Liu, Y.; Granger, E.; Desrosiers, C.; Zhang, X.; Ben Ayed, I.; Lu, H.B. Multiregion segmentation

of bladder cancer structures in MRI with progressive dilated convolutional networks.

Med. Phys.

2018,

45,

5482–5493. [CrossRef]

[PubMed]

442. Barentsz, J.O.; Jager, G.J.; Witjes, J.A.; Ruijs, J.H.J. Primary staging of urinary bladder carcinoma: The role of MRI and a comparison

with ct.

Eur. Radiol.

1996,

129–133. [CrossRef]

443. Mehraj, A.; Hameed, A.; Nanda, S.; Wazir, B.S.; Khan, M.A. Role of MRI in staging urinary bladder tumors.

Ann. Med. Health Sci.

Res.

2017,

116–121.

444. Abouelkheir, R.T.; Abdelhamid, A.; Abou El-Ghar, M.; El-Diasty, T. Imaging of bladder cancer: Standard applications and future

trends.

Med. Lith.

2021,

57,

220. [CrossRef]

445. Wong, V.C.K.; Ganeshan, D.; Jensen, C.T.; Devine, C.E. Imaging and management of bladder cancer.

Cancers

2021,

13,

1396.

[CrossRef]

446. Woo, S.; Suh, C.H.; Kim, S.Y.; Cho, J.Y.; Kim, S.H. Diagnostic performance of MRI for prediction of muscle-invasiveness of bladder

cancer: A systematic review and meta-analysis.

Eur. J. Radiol.

2017,

95,

46–55. [CrossRef] [PubMed]

447. Ghafoori, M.; Shakiba, M.; Ghiasi, A.; Asvadi, N.; Hosseini, K.; Alavi, M. Value of MRI in local staging of bladder cancer.

Urol. J.

2013,

10,

866–872.

448. Rosenkrantz, A.B.; Mussi, T.C.; Melamed, J.; Taneja, S.S.; Huang, W.C. Bladder cancer: Utility of MRI in detection of occult

muscle-invasive disease.

Acta Radiol.

2012,

53,

695–699. [CrossRef] [PubMed]

449. Badawy, M.; Farg, H.; Gadelhak, B.; ElGhar, M.A.; Sadeq, A.G.; Borg, M. Diagnostic performance of 3-tesla multiparametric MRI

for assessment of the bladder cancer t stage and histologic grade.

Egypt. J. Radiol. Nucl. Med.

2020,

51,

11. [CrossRef]

450. Juri, H.; Narumi, Y.; Panebianco, V.; Osuga, K. Staging of bladder cancer with multiparametric MRI.

Br. J. Radiol.

2020,

93,

[CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

49 of 55

451. Naish, J.H.; McGrath, D.M.; Bains, L.J.; Passera, K.; Roberts, C.; Watson, Y.; Cheung, S.; Taylor, M.B.; Logue, J.P.; Buckley, D.L.;

et al. Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer.

Magn.

Reson. Med.

2011,

66,

219–226. [CrossRef]

452. Hijab, A.; Tocco, B.; Hanson, I.; Meijer, H.; Nyborg, C.J.; Bertelsen, A.S.; Smeenk, R.J.; Smith, G.; Michalski, J.; Baumann, B.C.; et al.

Mr-guided adaptive radiotherapy for bladder cancer.

Front. Oncol.

2021,

11,

12. [CrossRef]

453. Wollin, D.A.; Deng, F.M.; Huang, W.C.; Babb, J.S.; Rosenkrantz, A.B. Conventional and diffusion-weighted MRI features in

diagnosis of metastatic lymphadenopathy in bladder cancer.

Can. J. Urol.

2014,

21,

7454–7459.

454. Watanabe, H.; Kanematsu, M.; Kondo, H.; Goshima, S.; Tsuge, Y.; Onozuka, M.; Moriyama, N. Preoperative t staging of urinary

bladder cancer: Does diffusion-weighted MRI have supplementary value?

Am. J. Roentgenol.

2009,

192,

1361–1366. [CrossRef]

455. Howlader, N.; Noone, A.M.; Krapcho, M.; Miller, D.; Bishop, K.; Kosary, C.L.; Yu, M.; Ruhl, J.; Tatalovich, Z.; Mariotto, A.; et al.

Seer Cancer Statistics Review, 1975–2014,

April 2017 ed.; National Cancer Institute: Bethesda, MD, USA, 2017.

456. Sharma, S.K.; Nemieboka, B.; Sala, E.; Lewis, J.S.; Zeglis, B.M. Molecular imaging of ovarian cancer.

J. Nucl. Med.

2016,

57,

827–833. [CrossRef]

457. Engbersen, M.P.; van’ t Sant, I.; Lok, C.; Lambregts, D.M.J.; Sonke, G.S.; Beets-Tan, R.G.H.; van Driel, W.J.; Lahaye, M.J. MRI with

diffusion-weighted imaging to predict feasibility of complete cytoreduction with the peritoneal cancer index (PCI) in advanced

stage ovarian cancer patients.

Eur. J. Radiol.

2019,

114,

146–151. [CrossRef] [PubMed]

458. Michielsen, K.; Dresen, R.; Vanslembrouck, R.; De Keyzer, F.; Amant, F.; Mussen, E.; Leunen, K.; Berteloot, P.; Moerman, P.;

Vergote, I.; et al. Diagnostic value of whole body diffusion-weighted MRI compared to computed tomography for pre-operative

assessment of patients suspected for ovarian cancer.

Eur. J. Cancer

2017,

83,

88–98. [CrossRef] [PubMed]

459. Rockall, A.G. Diffusion weighted MRI in ovarian cancer.

Curr. Opin. Oncol.

2014,

26,

529–535. [CrossRef] [PubMed]

460. Carter, J.S.; Koopmeiners, J.S.; Kuehn-Hajder, J.E.; Metzger, G.J.; Lakkadi, N.; Downs, L.S.; Bolan, P.J. Quantitative multiparametric

MRI of ovarian cancer.

J. Magn. Reson. Imaging

2013,

38,

1501–1509. [CrossRef] [PubMed]

461. Mikkelsen, M.S.; Petersen, L.K.; Blaakaer, J.; Marinovskij, E.; Rosenkilde, M.; Andersen, G.; Bouchelouche, K.; Iversen, L.H.

Assessment of peritoneal metastases with DW-MRI, CT, and FDG PET/CT before cytoreductive surgery for advanced stage

epithelial ovarian cancer.

Eur. J. Surg. Oncol.

2021,

47,

2134–2141. [CrossRef]

462. Khiewvan, B.; Torigian, D.A.; Emamzadehfard, S.; Paydary, K.; Salavati, A.; Houshmand, S.; Werner, T.J.; Alavi, A. An update on

the role of PET/CT and PET/MRI in ovarian cancer.

Eur. J. Nucl. Med. Mol. Imaging

2017,

44,

1079–1091. [CrossRef] [PubMed]

463. Tsuyoshi, H.; Tsujikawa, T.; Yamada, S.; Okazawa, H.; Yoshida, Y. Diagnostic value of [

F]FDG PET/MRI for staging in patients

with ovarian cancer.

EJNMMI Res.

2020,

10,

117. [CrossRef]

464. Kim, C.K.; Park, B.K.; Choi, J.Y.; Kim, B.G.; Han, H. Detection of recurrent ovarian cancer at MRI: Comparison with integrated

PET/CT.

J. Comput. Assist. Tomogr.

2007,

31,

868–875. [CrossRef]

465. Schmidt, S.; Meuli, R.A.; Achtari, C.; Prior, J.O. Peritoneal carcinomatosis in primary ovarian cancer staging comparison between

mdct, MRI, and

F-FDG PET/CT.

Clin. Nucl. Med.

2015,

40,

371–377. [CrossRef]

466. Sanli, Y.; Turkmen, C.; Bakir, B.; Iyibozkurt, C.; Ozel, S.; Has, D.; Yilmaz, E.; Topuz, S.; Yavuz, E.; Unal, S.N.; et al. Diagnostic

value of PET/CT is similar to that of conventional MRI and even better for detecting small peritoneal implants in patients with

recurrent ovarian cancer.

Nucl. Med. Commun.

2012,

33,

509–515. [CrossRef]

467. Nam, E.J.; Yun, M.J.; Oh, Y.T.; Kim, J.W.; Kim, J.H.; Kim, S.; Jung, Y.W.; Kim, S.W.; Kim, Y.T. Diagnosis and staging of primary

ovarian cancer: Correlation between PET/CT, doppler us, and CT or MRI.

Gynecol. Oncol.

2010,

116,

389–394. [PubMed]

468. Reinhold, C.; Rockall, A.; Sadowski, E.A.; Siegelman, E.S.; Maturen, K.E.; Vargas, H.A.; Forstner, R.; Glanc, P.; Andreotti, R.F.;

Thomassin-Naggara, I. Ovarian-adnexal reporting lexicon for MRI: A white paper of the acr ovarian-adnexal reporting and data

systems MRI committee.

J. Am. Coll. Radiol.

2021,

18,

713–729. [CrossRef] [PubMed]

469. Murakami, N.; Ando, K.; Murata, M.; Murata, K.; Ohno, T.; Aoshika, T.; Kato, S.; Okonogi, N.; Saito, A.I.; Kim, J.Y.; et al. Why not

de-intensiﬁcation for uterine cervical cancer?

Gynecol. Oncol.

2021,

163,

105–109. [CrossRef] [PubMed]

470. Novellas, S.; Fournol, M.; Marcotte-Bloch, C.; Mondot, L.; Caramella, T.; Bongain, A.; Chevallier, P. Magnetic resonance staging of

uterine cervix carcinoma.

Feuill. Rad.

2008,

48,

147–154. [CrossRef]

471. Bourgioti, C.; Chatoupis, K.; Moulopoulos, L.A. Current imaging strategies for the evaluation of uterine cervical cancer.

World J.

Radiol.

2016,

342–354. [CrossRef]

472. Theodore, C.; Levaillant, J.M.; Capmas, P.; Chabi, N.; Skalli, D.; Vienet-Legue, L.; Haddad, B.; Fernandez, H.; Touboul, C. MRI

and ultrasound fusion imaging for cervical cancer.

Anticancer Res.

2017,

37,

5079–5085.

473. Marzouk, F.; Jalaguier-Coudray, A.; Villard-Mahjoub, R. Uterine cervical cancer: The new ﬁgo classiﬁcation.

Imaging Femme

2021,

31,

30–39.

474. Kasuya, G.; Toita, T.; Furutani, K.; Kodaira, T.; Ohno, T.; Kaneyasu, Y.; Yoshimura, R.; Uno, T.; Yogi, A.; Ishikura, S.; et al.

Distribution patterns of metastatic pelvic lymph nodes assessed by ct/MRI in patients with uterine cervical cancer.

Radiat. Oncol.

2013,

6. [CrossRef]

475. Zhu, Y.X.; Shen, B.M.; Pei, X.; Liu, H.X.; Li, G.Y. Ct, MRI, and PET imaging features in cervical cancer staging and lymph node

metastasis.

Am. J. Transl. Res.

2021,

13,

10536–10544.

476. Grueneisen, J.; Schaarschmidt, B.M.; Heubner, M.; Aktas, B.; Kinner, S.; Forsting, M.; Lauenstein, T.; Ruhlmann, V.; Umutlu, L.

Integrated PET/MRI for whole-body staging of patients with primary cervical cancer: Preliminary results.

Eur. J. Nucl. Med. Mol.

Imaging

2015,

42,

1814–1824. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

50 of 55

477. Monteil, J.; Maubon, A.; Leobon, S.; Roux, S.; Marin, B.; Renaudie, J.; Genet, D.; Fermeaux, V.; Aubard, Y.; Tubiana-Mathieu, N.

Lymph node assessment with

F-FDG-PET and MRI in uterine cervical cancer.

Anticancer Res.

2011,

31,

3865–3871. [PubMed]

478. de Boer, P.; Adam, J.A.; Buist, M.R.; van de Vijver, M.J.; Rasch, C.R.; Stoker, J.; Bipat, S.; Stalpers, L.J.A. Role of MRI in detecting

involvement of the uterine internal os in uterine cervical cancer: Systematic review of diagnostic test accuracy.

Eur. J. Radiol.

2013,

82,

E422–E428. [CrossRef] [PubMed]

479. Kitajima, K.; Suenaga, Y.; Ueno, Y.; Kanda, T.; Maeda, T.; Deguchi, M.; Ebina, Y.; Yamada, H.; Takahashi, S.; Sugimura, K. Fusion

of PET and MRI for staging of uterine cervical cancer: Comparison with contrast-enhanced

F-FDG PET/CT and pelvic MRI.

Clin. Imaging

2014,

38,

464–469. [CrossRef] [PubMed]

480. Balleyguier, C.; Fournet, C.; Ben Hassen, W.; Zareski, E.; Morice, P.; Haie-Meder, C.; Uzan, C.; Gouy, S.; Duvillard, P.; Lhomme, C.

Management of cervical cancer detected during pregnancy: Role of magnetic resonance imaging.

Clin. Imaging

2013,

37,

70–76.

[CrossRef]

481. Surov, A.; Meyer, H.J.; Schob, S.; Hohn, A.K.; Bremicker, K.; Exner, M.; Stumpp, P.; Purz, S. Parameters of simultaneous

F-FDG-

PET/MRI predict tumor stage and several histopathological features in uterine cervical cancer.

Oncotarget

2017,

28285–28296.

[CrossRef]

482. Lee, J.; Kim, C.K.; Gu, K.W.; Park, W. Value of blood oxygenation level-dependent MRI for predicting clinical outcomes in uterine

cervical cancer treated with concurrent chemoradiotherapy.

Eur. Radiol.

2019,

29,

6256–6265. [CrossRef]

483. Saida, T.; Sakata, A.; Tanaka, Y.O.; Ochi, H.; Ishiguro, T.; Sakai, M.; Takahashi, H.; Satoh, T.; Minami, M. Clinical and MRI

characteristics of uterine cervical adenocarcinoma: Its variants and mimics.

Korean J. Radiol.

2019,

20,

14. [CrossRef]

484. Steiner, A.; Narva, S.; Rinta-Kiikka, I.; Hietanen, S.; Hynninen, J.; Virtanen, J. Diagnostic efﬁciency of whole-body

F-FDG

PET/MRI, MRI alone, and suv and adc values in staging of primary uterine cervical cancer.

Cancer Imaging

2021,

21,

11. [CrossRef]

485. Ran, C.; Sun, J.; Qu, Y.H.; Long, N. Clinical value of MRI, serum scca, and ca125 levels in the diagnosis of lymph node metastasis

and para-uterine inﬁltration in cervical cancer.

World J. Surg. Oncol.

2021,

19,

11. [CrossRef]

486. Park, B.K.; Kim, T.J. Useful MRI ﬁndings for minimally invasive surgery for early cervical cancer.

Cancers

2021,

13,

4078.

[CrossRef]

487. Hauge, A.; Wegner, C.S.; Gaustad, J.V.; Simonsen, T.G.; Andersen, L.M.K.; Rofstad, E.K. Diffusion-weighted MRI-derived adc

values reﬂect collagen i content in pdx models of uterine cervical cancer.

Oncotarget

2017,

105682–105691. [CrossRef] [PubMed]

488. Liu, Y.; Ye, Z.X.; Sun, H.R.; Bai, R.J. Clinical application of diffusion-weighted magnetic resonance imaging in uterine cervical

cancer.

Int. J. Gynecol. Cancer

2015,

25,

1073–1078. [CrossRef] [PubMed]

489. Liu, Y.; Liu, H.D.; Bai, X.; Ye, Z.X.; Sun, H.R.; Bai, R.J.; Wang, D.H. Differentiation of metastatic from non-metastatic lymph nodes

in patients with uterine cervical cancer using diffusion-weighted imaging.

Gynecol. Oncol.

2011,

122,

19–24. [CrossRef] [PubMed]

490. Liu, Y.; Bai, R.J.; Sun, H.R.; Liu, H.D.; Wang, D.H. Diffusion-weighted magnetic resonance imaging of uterine cervical cancer.

Comput. Assist. Tomogr.

2009,

33,

858–862.

491. Hallac, R.R.; Ding, Y.; Yuan, Q.; McColl, R.W.; Lea, J.; Sims, R.D.; Weatherall, P.T.; Mason, R.P. Oxygenation in cervical cancer

and normal uterine cervix assessed using blood oxygenation level-dependent (bold) MRI at 3t.

Nmr Biomed.

2012,

25,

1321–1330.

[CrossRef]

492. Ryan, A.J.; Susil, B.; Jobling, T.W.; Oehler, M.K. Endometrial cancer.

Cell Tissue Res.

2005,

322,

53–61. [CrossRef]

493. Soneji, N.D.; Bharwani, N.; Ferri, A.; Stewart, V.; Rockall, A. Pre-operative MRI staging of endometrial cancer in a multicentre

cancer network: Can we match single centre study results?

Eur. Radiol.

2018,

28,

4725–4734. [CrossRef]

494. Taieb, S.; Rocourt, N.; Narducci, F.; Ceugnart, L. Endometrial cancer imaging.

Bull. Cancer

2012,

99,

13–20.

495. Nougaret, S.; Horta, M.; Sala, E.; Lakhman, Y.; Thomassin-Naggara, I.; Kido, A.; Masselli, G.; Bharwani, N.; Sadowski, E.; Ertmer,

A.; et al. Endometrial cancer MRI staging: Updated guidelines of the european society of urogenital radiology.

Eur. Radiol.

2019,

29,

792–805. [CrossRef]

496. Meissnitzer, M.; Forstner, R. MRI of endometrium cancer—How we do it.

Cancer Imaging

2016,

16,

9. [CrossRef]

497. Ortoft, G.; Dueholm, M.; Mathiesen, O.; Hansen, E.S.; Lundorf, E.; Moller, C.; Marinovskij, E.; Petersen, L.K. Preoperative staging

of endometrial cancer using tvs, MRI, and hysteroscopy.

Acta Obstet. Gynecol. Scand.

2013,

92,

536–545. [CrossRef] [PubMed]

498. Sadowski, E.A.; Robbins, J.B.; Guite, K.; Patel-Lippmann, K.; del Rio, A.M.; Kushner, D.M.; Al-Niaimi, A. Preoperative pelvic MRI

and serum cancer antigen-125: Selecting women with grade 1 endometrial cancer for lymphadenectomy.

Am. J. Roentgenol.

2015,

205,

W556–W564. [CrossRef] [PubMed]

499. Tanaka, T.; Terai, Y.; Ono, Y.J.; Fujiwara, S.; Tanaka, Y.; Sasaki, H.; Tsunetoh, S.; Kanemura, M.; Yamamoto, K.; Yamada, T.; et al.

Preoperative MRI and intraoperative frozen section diagnosis of myometrial invasion in patients with endometrial cancer.

Int. J.

Gynecol. Cancer

2015,

25,

879–883. [CrossRef] [PubMed]

500. Abu Freij, M.; Saleh, H.; Rawlins, H.; Duncan, T.; Nieto, J. The use of MRI for selecting patients with endometrial cancer and

signiﬁcant co-morbidities for vaginal hysterectomy.

Arch. Gynecol. Obstet.

2011,

283,

1097–1101. [CrossRef] [PubMed]

501. Ben-Shachar, I.; Vitellas, K.M.; Cohn, D.E. The role of MRI in the conservative management of endometrial cancer.

Gynecol. Oncol.

2004,

93,

233–237. [CrossRef]

502. Bakir, B.; Sanli, S.; Bakir, V.L.; Ayas, S.; Yildiz, S.O.; Iyibozkurt, A.C.; Kartal, M.G.; Yavuz, E. Role of diffusion weighted MRI in

the differential diagnosis of endometrial cancer, polyp, hyperplasia, and physiological thickening.

Clin. Imaging

2017,

41,

86–94.

[CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

51 of 55

503. Zhou, Y.; Jiang, L.; Nuerlan, T. Application of diffusion-weighted MRI for endometrial cancer diagnosis.

Eur. J. Gynaecol. Oncol.

2018,

39,

655–658.

504. Ma, Q.L.; Wang, H.Y.; Tao, C.; Duan, F.; Li, X.L. Evaluation of 3.0t multimodal MRI in patients with endometrial cancer.

Acta Med.

Mediterr.

2021,

37,

1887–1892.

505. Chen, J.Y.; Gu, H.L.; Fan, W.M.; Wang, Y.H.; Chen, S.; Chen, X.; Wang, Z.Q. MRI-based radiomic model for preoperative risk

stratiﬁcation in stage I endometrial cancer.

J. Cancer

2021,

12,

726–734. [CrossRef]

506. Fasmer, K.E.; Hodneland, E.; Dybvik, J.A.; Wagner-Larsen, K.; Trovik, J.; Salvesen, O.; Krakstad, C.; Haldorsen, I.H.S. Whole-

volume tumor MRI radiomics for prognostic modeling in endometrial cancer.

J. Magn. Reson. Imaging

2021,

53,

928–937.

[CrossRef]

507. Keles, D.K.; Evrimler, S.; Merd, N.; Erdemoglu, E. Endometrial cancer: The role of MRI quantitative assessment in preoperative

staging and risk stratiﬁcation.

Acta Radiol

2021.

[CrossRef] [PubMed]

508. Chen, J.Y.; Fan, W.M.; Gu, H.L.; Zhang, W.; Liu, Y.T.; Wang, Y.J.; Pan, Z.C.; Wang, Z.Q. Preoperative MRI and immunohistochemical

examination for the prediction of high-risk endometrial cancer.

Gland Surg.

2021,

10,

2180–2191. [CrossRef] [PubMed]

509. Kim, H.J.; Cho, A.; Yun, M.; Kim, Y.T.; Kang, W. Comparison of FDG PET/CT and MRI in lymph node staging of endometrial

cancer.

Ann. Nucl. Med.

2016,

30,

104–113. [CrossRef] [PubMed]

510. Tsuyoshi, H.; Tsujikawa, T.; Yamada, S.; Okazawa, H.; Yoshida, Y. Diagnostic value of

F-FDG PET/MRI for staging in patients

with endometrial cancer.

Cancer Imaging

2020,

20,

9. [CrossRef] [PubMed]

511. Bezzi, C.; Zambella, E.; Ghezzo, S.; Fallanca, F.; Samanes Gajate, A.M.; Franchini, A.; Ironi, G.; Bergamini, A.; Monaco, L.;

Evangelista, L.; et al.

F-FDG PET/MRI in endometrial cancer: Systematic review and meta-analysis.

Clin. Transl. Imaging

2021,

1–14. [CrossRef]

512. Kitajima, K.; Suenaga, Y.; Ueno, Y.; Kanda, T.; Maeda, T.; Takahashi, S.; Ebina, Y.; Miyahara, Y.; Yamada, H.; Sugimura, K. Value of

fusion of PET and MRI for staging of endometrial cancer: Comparison with

F-FDG contrast-enhanced PET/CT and dynamic

contrast-enhanced pelvic MRI.

Eur. J. Radiol.

2013,

82,

1672–1676. [CrossRef]

513. Bian, L.H.; Wang, M.; Gong, J.; Liu, H.H.; Wang, N.; Wen, N.; Fan, W.S.; Xu, B.X.; Wang, M.Y.; Ye, M.X.; et al. Comparison of

integrated PET/MRI with PET/ct in evaluation of endometrial cancer: A retrospective analysis of 81 cases.

PeerJ

2019,

10.

[CrossRef]

514. Hama, Y.; Tate, E. Palliative MRI-guided intensity modulated radiation therapy (imrt) for locally recurrent endometrial cancer.

Aktualni Gynekol. Porod.

2020,

12,

70–74.

515. Whitaker, H.; Tam, J.O.; Connor, M.J.; Grey, A. Prostate cancer biology & genomics.

Transl. Androl. Urol.

2020,

1481–1491.

516. Shukla-Dave, A.; Hricak, H. Role of MRI in prostate cancer detection.

NMR Biomed.

2014,

27,

16–24. [CrossRef]

517. Hotker, A.M.; Dappa, E.; Mazaheri, Y.; Ehdaie, B.; Zheng, J.T.; Capanu, M.; Hricak, H.; Akin, O. The inﬂuence of background

signal intensity changes on cancer detection in prostate MRI.

Am. J. Roentgenol.

2019,

212,

823–829. [CrossRef] [PubMed]

518. Ventrella, E.; Eusebi, L.; Carpagnano, F.A.; Bartelli, F.; Cormio, L.; Guglielmi, G. Multiparametric MRI of prostate cancer: Recent

advances.

Curr. Radiol. Rep.

2020,

14. [CrossRef]

519. Sun, Y.; Reynolds, H.M.; Parameswaran, B.; Wraith, D.; Finnegan, M.E.; Williams, S.; Haworth, A. Multiparametric MRI and

radiomics in prostate cancer: A review.

Australas. Phys. Eng. Sci. Med.

2019,

42,

3–25. [CrossRef] [PubMed]

520. Kumar, V.; Bora, G.S.; Kumar, R.; Jagannathan, N.R. Multiparametric (MP) MRI of prostate cancer.

Prog. Nucl. Magn. Reson.

Spectrosc.

2018,

105,

23–40. [CrossRef] [PubMed]

521. Ghai, S.; Haider, M.A. Multiparametric-MRI in diagnosis of prostate cancer.

Indian J. Urol.

2015,

31,

194–201. [CrossRef]

522. Wu, R.C.; Lebastchi, A.H.; Hadaschik, B.A.; Emberton, M.; Moore, C.; Laguna, P.; Futterer, J.J.; George, A.K. Role of MRI for the

detection of prostate cancer.

World J. Urol.

2021,

39,

637–649. [CrossRef]

523. Piert, M.; El Naqa, I.; Davenport, M.S.; Incerti, E.; Mapelli, P.; Picchio, M. Pet/MRI and prostate cancer.

Clin. Transl. Imaging

2016,

473–485. [CrossRef]

524. Park, H.; Wood, D.; Hussain, H.; Meyer, C.R.; Shah, R.B.; Johnson, T.D.; Chenevert, T.; Piert, M. Introducing parametric fusion

PET/MRI of primary prostate cancer.

J. Nucl. Med.

2012,

53,

546–551. [CrossRef]

525. Lindenberg, L.; Ahlman, M.; Turkbey, B.; Mena, E.; Choyke, P. Evaluation of prostate cancer with PET/MRI.

J. Nucl. Med.

2016,

57,

111S–116S. [CrossRef]

526. Kreydin, E.I.; Barrisford, G.W.; Feldman, A.S.; Preston, M.A. Testicular cancer: What the radiologist needs to know.

Am. J.

Roentgenol.

2013,

200,

1215–1225. [CrossRef]

527. Nestler, T.; Baunacke, M.; Drager, D.; von Landenberg, N.; Groeben, C.; Huber, J. Testicular cancer guideline adherence and

patterns of care in germany: A nationwide survey.

Eur. J. Cancer Care

2019,

28,

9. [CrossRef] [PubMed]

528. Hale, G.R.; Teplitsky, S.; Truong, H.; Gold, S.A.; Bloom, J.B.; Agarwal, P.K. Lymph node imaging in testicular cancer.

Transl.

Androl. Urol.

2018,

864–874. [CrossRef] [PubMed]

529. Dulal, M.; Lei, J.; Dou, S.W.; Zhu, S.C. Role of imaging in testicular cancer.

Curr. Med. Imaging Rev.

2017,

13,

38–49. [CrossRef]

530. Rud, E.; Langberg, C.W.; Baco, E.; Lauritzen, P.; Sandbaek, G. MRI in the follow-up of testicular cancer: Less is more.

Anticancer

Res.

2019,

39,

2963–2968. [CrossRef] [PubMed]

531. Tsili, A.C.; Soﬁkitis, N.; Stiliara, E.; Argyropoulou, M.I. MRI of testicular malignancies.

Abdom. Radiol.

2019,

44,

1070–1082.

[CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

52 of 55

532. Larsen, S.K.A.; Agerbaek, M.; Jurik, A.G.; Pedersen, E.M. Ten years of experience with MRI follow-up of testicular cancer stage i:

A retrospective study and an MRI protocol with dwi.

Acta Oncol.

2020,

59,

1374–1381. [CrossRef]

533. Manganaro, L.; Saldari, M.; Pozza, C.; Vinci, V.; Gianfrilli, D.; Greco, E.; Franco, G.; Sergi, M.E.; Scialpi, M.; Catalano, C.; et al.

Dynamic contrast-enhanced and diffusion-weighted mr imaging in the characterisation of small, non-palpable solid testicular

tumours.

Eur. Radiol.

2018,

28,

554–564. [CrossRef] [PubMed]

534. Laukka, M.; Mannisto, S.; Beule, A.; Kouri, M.; Blomqvist, C. Comparison between ct and MRI in detection of metastasis of the

retroperitoneum in testicular germ cell tumors: A prospective trial.

Acta Oncol.

2020,

59,

660–665. [CrossRef] [PubMed]

535. Reinges, M.H.T.; Kaiser, W.A.; Miersch, W.D.; Vogel, J.; Reiser, M. Dynamic MRI of benign and malignant testicular lesions—

Preliminary-observations.

Eur. Radiol.

1995,

615–622. [CrossRef]

536. Rocher, L.; Ksouri, A.; Maxwell, F.; Bresson, B.; Hindawi, G.; Balasa, C.; Bellin, M.F.; Albiges, L. Testicular tumors: A diagnostic

challenge of imaging.

Bull. Cancer

2019,

106,

875–886. [CrossRef]

537. Khanna, M.; Abualruz, A.R.; Yadav, S.K.; Mafraji, M.; Al-Rumaihi, K.; Al-Bozom, I.; Kumar, D.; Tsili, A.C.; Schieda, N. Diagnostic

performance of multi-parametric MRI to differentiate benign sex cord stromal tumors from malignant (non-stromal and stromal)

testicular neoplasms.

Abdom. Radiol.

2021,

46,

319–330. [CrossRef] [PubMed]

538. Sohaib, S.A.; Koh, D.M.; Barbachano, Y.; Parikh, J.; Husband, J.E.S.; Dearnaley, D.P.; Horwich, A.; Huddart, R. Prospective

assessment of MRI for imaging retroperitoneal metastases from testicular germ cell tumours.

Clin. Radiol.

2009,

64,

362–367.

[CrossRef] [PubMed]

539. Ortiz, A.F.H.; Beaujon, L.J.F.; Villamizar, S.Y.G.; Lopez, F.F.F. Magnetic resonance versus computed tomography for the detection

of retroperitoneal lymph node metastasis due to testicular cancer: A systematic literature review.

Eur. J. Radiol. Open

2021,

540. Kubik-Huch, R.A.; Hailemariam, S.; Hamm, B. Ct and MRI of the male genital tract: Radiologic-pathologic correlation.

Eur.

Radiol.

1999,

16–28. [CrossRef] [PubMed]

541. Bleiberg, H. Colorectal cancer: The challenge.

Eur. J. Cancer

1996,

32A,

S2–S6. [CrossRef]

542. de Souza, G.D.; Souza, L.R.Q.; Cuenca, R.M.; Vilela, V.M.; Santos, B.; de Aguiar, F.S. Pre- and postoperative imaging methods in

colorectal cancer.

ABCD-Arq. Bras. Cir. Dig.-Braz. Arch. Dig. Surg.

2018,

31,

5. [CrossRef]

543. Georgiou, P.A.; Tekkis, P.P.; Constantinides, V.A.; Patel, U.; Goldin, R.D.; Darzi, A.W.; Nicholls, R.J.; Brown, G. Diagnostic accuracy

and value of magnetic resonance imaging (MRI) in planning exenterative pelvic surgery for advanced colorectal cancer.

Eur. J.

Cancer

2013,

49,

72–81. [CrossRef]

544. Annemans, L.; Lencioni, R.; Warie, H.; Bartolozzi, C.; Ciceri, M.; Muller, U. Health economic evaluation of ferucarbotran-enhanced

MRI in the diagnosis of liver metastases in colorectal cancer patients.

Int. J. Colorectal Dis.

2008,

23,

77–83. [CrossRef]

545. Engbersen, M.P.; Rijsemus, C.J.V.; Nederend, J.; Aalbers, A.G.J.; de Hingh, I.; Retel, V.; Lambregts, D.M.J.; Van der Hoeven, E.;

Boerma, D.; Wiezer, M.J.; et al. Dedicated MRI staging versus surgical staging of peritoneal metastases in colorectal cancer

patients considered for crs-hipec; the disco randomized multicenter trial.

BMC Cancer

2021,

21,

8. [CrossRef]

546. Westberg, K.; Othman, B.; Suzuki, C.; Blomqvist, L.; Martling, A.; Iversen, H. Magnetic resonance imaging as a predictor of

surgical outcome in patients with local pelvic recurrence of colorectal cancer.

Eur. J. Surg. Oncol.

2021,

47,

2119–2124. [CrossRef]

547. Nasseri, Y.; Langenfeld, S.J. Imaging for colorectal cancer.

Surg. Clin.

2017,

97,

503–513. [CrossRef] [PubMed]

548. Dresen, R.C.; De Vuysere, S.; De Keyzer, F.; Van Cutsem, E.; Prenen, H.; Vanslembrouck, R.; De Hertogh, G.; Wolthuis, A.;

D’Hoore, A.; Vandecaveye, V. Whole-body diffusion-weighted MRI for operability assessment in patients with colorectal cancer

and peritoneal metastases.

Cancer Imaging

2019,

19,

10. [CrossRef] [PubMed]

549. Sabry, M.S.; Rady, A.E.E.; Niazi, G.E.M.; Ali, S.A. Role of diffusion-weighted MRI in diagnosis and post therapeutic follow-up of

colorectal cancer.

Egypt. J. Radiol. Nucl. Med.

2021,

52,

11. [CrossRef]

550. Ono, K.; Ochiai, R.; Yoshida, T.; Kitagawa, M.; Omagari, J.; Kobayashi, H.; Yamashita, Y. Comparison of diffusion-weighted MRI

and 2- ﬂuorine-18 -ﬂuoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) for detecting primary colorectal cancer

and regional lymph node metastases.

J. Magn. Reson. Imaging

2009,

29,

336–340. [CrossRef] [PubMed]

551. Kim, T.H.; Woo, S.; Han, S.; Suh, C.H.; Vargas, H.A. The diagnostic performance of MRI for detection of extramural venous

invasion in colorectal cancer: A systematic review and meta-analysis of the literature.

Am. J. Roentgenol.

2019,

213,

575–585.

[CrossRef] [PubMed]

552. Smith, N.J.; Shihab, O.; Arnaout, A.; Swift, R.I.; Brown, G. MRI for detection of extramural vascular invasion in rectal cancer.

Am.

J. Roentgenol.

2008,

191,

1517–1522. [CrossRef]

553. Soomro, M.H.; De Cola, G.; Conforto, S.; Schmid, M.; Giunta, G.; Guidi, E.; Neri, E.; Caruso, D.; Ciolina, M.; Laghi, A.; et al.

Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary

study. In Proceedings of the 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, 28–30

March 2018; pp. 198–203.

554. Ogawa, M.; Ichiba, N.; Watanabe, M.; Yanaga, K. The usefulness of diffusion MRI in detection of lymph node metastases of

colorectal cancer.

Anticancer Res.

2016,

36,

815–819.

555. Ichikawa, T.; Erturk, S.M.; Motosugi, U.; Sou, H.; Iino, H.; Araki, T.; Fujii, H. High-b-value diffusion-weighted MRI in colorectal

cancer.

Am. J. Roentgenol.

2006,

187,

181–184. [CrossRef]

556. García-Figueiras, R.; Baleato-González, S.; Canedo-Antelo, M.; Alcalá, L.; Marhuenda, A. Imaging advances on ct and MRI in

colorectal cancer.

Curr. Colorectal Cancer Rep.

2021,

17,

113–130. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

53 of 55

557. Kijima, S.; Sasaki, T.; Nagata, K.; Utano, K.; Lefor, A.T.; Sugimoto, H. Preoperative evaluation of colorectal cancer using ct

colonography, MRI, and PET/CT.

World J. Gastroenterol.

2014,

20,

16964–16975. [CrossRef]

558. Kulemann, V.; Schima, W.; Tamandl, D.; Kaczirek, K.; Gruenberger, T.; Wrba, F.; Weber, M.; Ba-Ssalamah, A. Preoperative detection

of colorectal liver metastases in fatty liver: MDCT or MRI?

Eur. J. Radiol.

2011,

79,

E1–E6. [CrossRef] [PubMed]

559. Kang, B.; Lee, J.M.; Song, Y.S.; Woo, S.; Hur, B.Y.; Jeon, J.H.; Paeng, J.C. Added value of integrated whole-body PET/MRI for

evaluation of colorectal cancer: Comparison with contrast-enhanced mdct.

Am. J. Roentgenol.

2016,

206,

W10–W20. [CrossRef]

[PubMed]

560. Schmidt, G. Importance of whole body MRI for staging of colorectal cancer.

Radiologe

2012,

52,

537–544. [CrossRef] [PubMed]

561. Datta, N.R.; Krishnan, S.; Speiser, D.E.; Neufeld, E.; Kuster, N.; Bodis, S.; Hofmann, H. Magnetic nanoparticle-induced hyperther-

mia with appropriate payloads: Paul ehrlich’s "magic (nano)bullet" for cancer theranostics?

Cancer Treat. Rev.

2016,

50,

217–227.

[CrossRef] [PubMed]

562. Douziech-Eyrolles, L.; Marchais, H.; Herve, K.; Munnier, E.; Souce, M.; Linassier, C.; Dubois, P.; Chourpa, I. Nanovectors for

anticancer agents based on superparamagnetic iron oxide nanoparticles.

Int. J. Nanomed.

2007,

541–550.

563. Balaita, L.; Popa, M. Polymer magnetic particles in biomedical applications.

Rev. Roum. Chim.

2009,

54,

185–199.

564. Rotariu, O.; Udrea, L.E.; Strachan, N.J.C.; Badescu, V. The guidance of magnetic colloids in simulated tissues for targeted drug

delivery.

J. Optoelectron. Adv. Mater.

2007,

942–945.

565. Alexiou, C.; Arnold, W.; Klein, R.J.; Parak, F.G.; Hulin, P.; Bergemann, C.; Erhardt, W.; Wagenpfeil, S.; Lubbe, A.S. Locoregional

cancer treatment with magnetic drug targeting.

Cancer Res.

2000,

60,

6641–6648.

566. Wahajuddin; Arora, S. Superparamagnetic iron oxide nanoparticles: Magnetic nanoplatforms as drug carriers.

Int. J. Nanomed.

2012,

3445–3471. [CrossRef]

567. Mansouri, M.; Nazarpak, M.H.; Solouk, A.; Akbari, S.; Hasani-Sadrabadi, M.M. Magnetic responsive of paclitaxel delivery system

based on spion and palmitoyl chitosan.

J. Magn. Magn. Mater.

2017,

421,

316–325. [CrossRef]

568. Yang, Y.; Guo, Q.F.; Peng, J.R.; Su, J.; Lu, X.L.; Zhao, Y.X.; Qian, Z.Y. Doxorubicin-conjugated heparin-coated superparamagnetic

iron oxide nanoparticles for combined anticancer drug delivery and magnetic resonance imaging.

J. Biomed. Nanotechnol.

2016,

12,

1963–1974. [CrossRef] [PubMed]

569. Mattingly, S.J.; O’Toole, M.G.; James, K.T.; Clark, G.J.; Nantz, M.H. Magnetic nanoparticle-supported lipid bilayers for drug

delivery.

Langmuir

2015,

31,

3326–3332. [CrossRef] [PubMed]

570. Ghorbani, M.; Hamishehkar, H.; Arsalani, N.; Entezami, A.A. Preparation of thermo and ph-responsive polymer@au/fe3o4

core/shell nanoparticles as a carrier for delivery of anticancer agent.

J. Nanopart. Res.

2015,

17,

13. [CrossRef]

571. Song, W.X.; Muthana, M.; Mukherjee, J.; Falconer, R.J.; Biggs, C.A.; Zhao, X.B. Magnetic-silk core-shell nanoparticles as potential

carriers for targeted delivery of curcumin into human breast cancer cells.

ACS Biomater. Sci. Eng.

2017,

1027–1038. [CrossRef]

572. Rotariu, O.; Udrea, L.E.; Strachan, N.J.C.; Badescu, V. Targeting magnetic carrier particles in tumour microvasculature—A

numerical study.

J. Optoelectron. Adv. Mater.

2005,

3209–3218.

573. Norris, M.D.; Seidel, K.; Kirschning, A. Externally induced drug release systems with magnetic nanoparticle carriers: An emerging

ﬁeld in nanomedicine.

Adv. Ther.

2019,

1800092. [CrossRef]

574. Ahmad, R.S.; Ali, Z.S.; Mou, X.B.; Wang, J.H.; Yi, H.; He, N.Y. Recent advances in magnetic nanoparticle design for cancer therapy.

J. Nanosci. Nanotechnol.

2016,

16,

9393–9403. [CrossRef]

575. Ramazanov, M.; Karimova, A.; Shirinova, H. Magnetism for drug delivery, MRI and hyperthermia applications: A review.

Biointerface Res. Appl. Chem.

2021,

11,

8654–8668.

576. Yang, H.Y.; Li, Y.; Lee, D.S. Multifunctional and stimuli-responsive magnetic nanoparticle-based delivery systems for biomedical

applications.

Adv. Ther.

2018,

1800011. [CrossRef]

577. Schneider-Futschik, E.K.; Reyes-Ortega, F. Advantages and disadvantages of using magnetic nanoparticles for the treatment of

complicated ocular disorders.

Pharmaceutics

2021,

13,

1157. [CrossRef] [PubMed]

578. Zamay, T.N.; Prokopenko, V.S.; Zamay, S.S.; Lukyanenko, K.A.; Kolovskaya, O.S.; Orlov, V.A.; Zamay, G.S.; Galeev, R.G.; Narodov,

A.A.; Kichkailo, A.S. Magnetic nanodiscs-a new promising tool for microsurgery of malignant neoplasms.

Nanomaterials

2021,

11,

1459. [CrossRef]

579. Golovin, Y.I.; Golovin, D.Y.; Vlasova, K.Y.; Veselov, M.M.; Usvaliev, A.D.; Kabanov, A.V.; Klyachko, N.L. Non-heating alternating

magnetic ﬁeld nanomechanical stimulation of biomolecule structures via magnetic nanoparticles as the basis for future low-toxic

biomedical applications.

Nanomaterials

2021,

11,

2255. [CrossRef]

580. Chen, M.W.; Wu, J.J.; Ning, P.; Wang, J.J.; Ma, Z.; Huang, L.Q.; Plaza, G.R.; Shen, Y.J.; Xu, C.; Han, Y.; et al. Remote control of

mechanical forces via mitochondrial-targeted magnetic nanospinners for efﬁcient cancer treatment.

Small

2020,

16,

14. [CrossRef]

[PubMed]

581. Alsharif, N.A.; Aleisa, F.A.; Liu, G.Y.; Ooi, B.S.; Patel, N.; Ravasi, T.; Merzaban, J.S.; Kosel, J. Functionalization of magnetic

nanowires for active targeting and enhanced cell-killing efﬁcacy.

ACS Appl. Bio Mater.

2020,

4789–4797. [CrossRef]

582. Chen, Y.; Han, P.; Wu, Y.; Zhang, Z.F.; Yue, Y.; Li, W.H.; Chu, M.Q. Hedgehog-like gold-coated magnetic microspheres that

strongly inhibit tumor growth through magnetomechanical force and photothermal effects.

Small

2018,

14,

13. [CrossRef]

583. Goiriena-Goikoetxea, M.; Munoz, D.; Orue, I.; Fernandez-Gubieda, M.L.; Bokor, J.; Muela, A.; Garcia-Arribas, A. Disk-shaped

magnetic particles for cancer therapy.

Appl. Phys. Rev.

2020,

15. [CrossRef]

Int. J. Mol. Sci.

2022,

23,

1339

54 of 55

584. Rahban, D.; Doostan, M.; Salimi, A. Cancer therapy; prospects for application of nanoparticles for magnetic-based hyperthermia.

Cancer Investig.

2020,

38,

507–521. [CrossRef]

585. Kok, H.P.; Cressman, E.N.K.; Ceelen, W.; Brace, C.L.; Ivkov, R.; Grull, H.; ter Haar, G.; Wust, P.; Crezee, J. Heating technology for

malignant tumors: A review.

Int. J. Hyperth.

2020,

37,

711–741. [CrossRef]

586. Liu, X.; Zhang, Y.; Wang, Y.; Zhu, W.; Li, G.; Ma, X.; Zhang, Y.; Chen, S.; Tiwari, S.; Shi, K.; et al. Comprehensive understanding of

magnetic hyperthermia for improving antitumor therapeutic efﬁcacy.

Theranostics

2020,

10,

3793–3815. [CrossRef]

587. Chang, D.; Lim, M.; Goos, J.; Qiao, R.; Ng, Y.Y.; Mansfeld, F.M.; Jackson, M.; Davis, T.P.; Kavallaris, M. Biologically targeted

magnetic hyperthermia: Potential and limitations.

Front. Pharm.

2018,

831. [CrossRef]

588. Frazier, N.; Ghandehari, H. Hyperthermia approaches for enhanced delivery of nanomedicines to solid tumors.

Biotechnol. Bioeng.

2015,

112,

1967–1983. [CrossRef] [PubMed]

589. Lee, Y.K.; Lee, S.B.; Kim, Y.U.; Kim, K.N.; Choi, S.Y.; Lee, K.H.; Shim, I.B.; Kim, C.S. Effect of ferrite thermoseeds on destruction of

carcinoma cells under alternating magnetic ﬁeld.

J. Mater. Sci.

2003,

38,

4221–4233. [CrossRef]

590. Shido, Y.; Nishida, Y.; Suzuki, Y.; Kobayashi, T.; Ishiguro, N. Targeted hyperthermia using magnetite cationic liposomes and an

alternating magnetic ﬁeld in a mouse osteosarcoma model.

J. Bone Jt. Surg. Br. Vol.

2010,

92B,

580–585. [CrossRef]

591. Manohar, A.; Vijayakanth, V.; Pallavolu, M.R.; Kim, K.H. Effects of Ni—Substitution on structural, magnetic hyperthermia,

photocatalytic and cytotoxicity study of MgFe

nanoparticles.

J. Alloys Compd.

2021,

879,

160515. [CrossRef]

592. Chen, S.Z.; Han, F.S.; Huang, D.D.; Meng, J.Q.; Chu, J.P.; Wang, M.; Wang, P.J. Fe

magnetic nanoparticle-enhanced radiotherapy

for lung adenocarcinoma via delivery of siBIRC5 and AS-ODN.

J. Transl. Med.

2021,

19,

1–14. [CrossRef]

593. Jiang, K.Y.; Zhang, Q.B.; Hinojosa, D.T.; Zhang, L.L.; Xiao, Z.; Yin, Y.; Tong, S.; Colvin, V.L.; Bao, G. Controlled oxidation and

surface modiﬁcation increase heating capacity of magnetic iron oxide nanoparticles.

Appl. Phys. Rev.

2021,

031407. [CrossRef]

594. Simeonidis, K.; Kaprara, E.; Rivera-Gil, P.; Xu, R.X.; Teran, F.J.; Kokkinos, E.; Mitropoulos, A.; Maniotis, N.; Balcells, L. Hydrotalcite-

embedded magnetite nanoparticles for hyperthermia-triggered chemotherapy.

Nanomaterials

2021,

11,

1796. [CrossRef]

595. Kawashita, M.; Domi, S.; Saito, Y.; Aoki, M.; Ebisawa, Y.; Kokubo, T.; Saito, T.; Takano, M.; Araki, N.; Hiraoka, M. In vitro heat

generation by ferrimagnetic maghemite microspheres for hyperthermic treatment of cancer under an alternating magnetic ﬁeld.

Mater. Sci. Mater. Med.

2008,

19,

1897–1903. [CrossRef]

596. Tietze, R.; Zaloga, J.; Unterweger, H.; Lyer, S.; Friedrich, R.P.; Janko, C.; Pottler, M.; Durr, S.; Alexiou, C. Magnetic nanoparticle-

based drug delivery for cancer therapy.

Biochem. Biophys. Res. Commun.

2015,

468,

463–470. [CrossRef]

597. Zhi, D.F.; Yang, T.; Yang, J.; Fu, S.; Zhang, S.B. Targeting strategies for superparamagnetic iron oxide nanoparticles in cancer

therapy.

Acta Biomater.

2020,

102,

13–34. [CrossRef]

598. Uskokovic, V.; Drofenik, M. Gold-embellished mixed-valence manganite as a smart, self-regulating magnetoplasmonic nanomate-

rial.

Mater. Chem. Phys.

2021,

271,

124870. [CrossRef]

599. Rodrigues, H.F.; Capistrano, G.; Bakuzis, A.F. In vivo magnetic nanoparticle hyperthermia: A review on preclinical studies,

low-ﬁeld nano-heaters, noninvasive thermometry and computer simulations for treatment planning.

Int. J. Hyperth.

2020,

37,

76–99. [CrossRef] [PubMed]

600. Rotariu, O.; Iacob, G.; Strachan, N.J.C.; Chiriac, H. Simulating the embolization of blood vessels using magnetic microparticles

and acupuncture needle in a magnetic ﬁeld.

Biotechnol. Prog.

2004,

20,

299–305. [CrossRef] [PubMed]

601. Anani, T.; Rahmati, S.; Sultana, N.; David, A.E. MRI-traceable theranostic nanoparticles for targeted cancer treatment.

Theranostics

2021,

11,

579–601. [CrossRef]

602. Kozissnik, B.; Bohorquez, A.C.; Dobson, J.; Rinaldi, C. Magnetic ﬂuid hyperthermia: Advances, challenges, and opportunity.

Int.

J. Hyperth.

2013,

29,

706–714. [CrossRef]

603. Xie, J.; Yan, C.; Yan, Y.; Chen, L.; Song, L.; Zang, F.; An, Y.; Teng, G.; Gu, N.; Zhang, Y. Multi-modal mn–zn ferrite nanocrystals for

magnetically-induced cancer targeted hyperthermia: A comparison of passive and active targeting effects.

Nanoscale

2016,

16902–16915. [CrossRef]

604. Angelakeris, M. Magnetic nanoparticles: A multifunctional vehicle for modern theranostics.

Biochim. Biophys. Acta Gen. Subj.

2017,

1861,

1642–1651. [CrossRef] [PubMed]

605. Cheng, H.W.; Tsao, H.Y.; Chiang, C.S.; Chen, S.Y. Advances in magnetic nanoparticle-mediated cancer immune-theranostics.

Adv.

Healthc. Mater.

2021,

10,

2001451. [CrossRef]

606. Rajan, A.; Sahu, N.K. Review on magnetic nanoparticle-mediated hyperthermia for cancer therapy.

J. Nanopart. Res.

2020,

22,

1–25. [CrossRef]

607. Soetaert, F.; Korangath, P.; Serantes, D.; Fiering, S.; Ivkov, R. Cancer therapy with iron oxide nanoparticles: Agents of thermal and

immune therapies.

Adv. Drug Deliv. Rev.

2020,

163,

65–83. [CrossRef]

608. Li, X.X.; Li, W.Y.; Wang, M.N.; Liao, Z.H. Magnetic nanoparticles for cancer theranostics: Advances and prospects.

J. Control.

Release

2021,

335,

437–448. [CrossRef] [PubMed]

609. Mohammed, L.; Gomaa, H.G.; Ragab, D.; Zhu, J. Magnetic nanoparticles for environmental and biomedical applications: A

review.

Particuology

2017,

30,

1–14. [CrossRef]

610. Li, Y.H.; Wang, N.; Huang, X.M.; Li, F.Y.; Davis, T.P.; Qiao, R.R.; Ling, D.S. Polymer-assisted magnetic nanoparticle assemblies for

biomedical applications.

ACS Appl. Bio Mater.

2020,

121–142. [CrossRef] [PubMed]

Int. J. Mol. Sci.

2022,

23,

1339

55 of 55

611. Pooam, M.; Jourdan, N.; El Esawi, M.; Sherrard, R.M.; Ahmad, M. Hek293 cell response to static magnetic ﬁelds via the radical

pair mechanism may explain therapeutic effects of pulsed electromagnetic ﬁelds.

PLoS ONE

2020,

15,

e0243038. [CrossRef]

612. Chatterjee, R.; Chatterjee, J. Ros and oncogenesis with special reference to emt and stemness.

Eur. J. Cell Biol.

2020,

99,

[CrossRef]