Vol 28, No 6 (2023)
Review paper
Published online: 2024-01-03

open access

Page views 298
Article views/downloads 120
Get Citation

Connect on Social Media

Connect on Social Media

Brain diffusion MRI biomarkers after oncology treatments

Mahdi Mohammadi1, Shabnam Banisharif2, Fatemeh Moradi3, Maryam Zamanian2, Ghazal Tanzifi4, Sadegh Ghaderi5
Rep Pract Oncol Radiother 2023;28(6):823-834.

Abstract

In addition to providing a measurement of the tumor’s size and dimensions, magnetic resonance imaging (MRI) provides excellent noninvasive radiographic detection of tumor location. The MRI technique is an important modality that has been shown to be useful in the prognosis, diagnosis, treatment planning, and evaluation of response and recurrence in solid cancers. Diffusion-weighted imaging (DWI) is an imaging technique that quantifies water mobility. This imaging approach is good for identifying sub-voxel microstructure of tissues, correlates with tumor cellularity, and has been proven to be valuable in the early assessment of cytotoxic treatment for a variety of malignancies. Diffusion tensor imaging (DTI) is an MRI method that assesses the preferred amount of water transport inside tissues. This enables precise measurements of water diffusion, which changes according to the direction of white matter fibers, their density, and myelination.  This measurement corresponds to some related variables: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and others. DTI biomarkers can detect subtle changes in white matter microstructure and integrity following radiation therapy (RT) or chemoradiotherapy, which may have implications for cognitive function and quality of life. In our study, these indices were evaluated after brain chemoradiotherapy.

Review article

Reports of Practical Oncology and Radiotherapy

2023, Volume 28, Number 6, pages: 823–834

DOI: 10.5603/rpor.98728

Submitted: 11.05.2022

Accepted: 04.12.2023

© 2023 Greater Poland Cancer Centre.

Published by Via Medica.

All rights reserved.

e-ISSN 2083–4640

ISSN 1507–1367

Brain diffusion MRI biomarkers after oncology treatments

Mahdi Mohammadi1Shabnam Banisharif2Fatemeh Moradi3Maryam Zamanian2Ghazal Tanzifi4Sadegh Ghaderi5
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
2Department of Medical Physics, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran
3Department of Energy Engineering & Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
4Department of Nuclear Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
5Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran

Address for correspondence: Sadegh Ghaderi, Tehran University of Medical Sciences, Department of Neuroscience and Addiction Studies, Tehran, Iran; e-mail: S_ghaderi@razi.tums.ac.ir

This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially

Abstract
In addition to providing a measurement of the tumor’s size and dimensions, magnetic resonance imaging (MRI) provides excellent noninvasive radiographic detection of tumor location. The MRI technique is an important modality that has been shown to be useful in the prognosis, diagnosis, treatment planning, and evaluation of response and recurrence in solid cancers. Diffusion-weighted imaging (DWI) is an imaging technique that quantifies water mobility. This imaging approach is good for identifying sub-voxel microstructure of tissues, correlates with tumor cellularity, and has been proven to be valuable in the early assessment of cytotoxic treatment for a variety of malignancies. Diffusion tensor imaging (DTI) is an MRI method that assesses the preferred amount of water transport inside tissues. This enables precise measurements of water diffusion, which changes according to the direction of white matter fibers, their density, and myelination. This measurement corresponds to some related variables: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and others. DTI biomarkers can detect subtle changes in white matter microstructure and integrity following radiation therapy (RT) or chemoradiotherapy, which may have implications for cognitive function and quality of life. In our study, these indices were evaluated after brain chemoradiotherapy.
Key words: diffusion MRI; brain; chemoradiotherapy; imaging biomarkers; neuroimaging
Rep Pract Oncol Radiother 2023;28(6):823–834

Introduction

Diffusion-weighted imaging (DWI) quantifies an estimate of water mobility obtained by magnetic resonance imaging (MRI), is useful for assessing sub-voxel microstructure in tissues, correlates with tumor cellularity, and has been shown to be useful in the early evaluation of cytotoxic therapy in a variety of cancers [1–5].

Diffusion tensor imaging (DTI) is a non-invasive MRI-based approach that detects white matter structure more accurately than conventional MRI. Water diffusion in tissues is measured using DTI, an MRI method that analyzes the preferred direction and amount of the water’s movement. Water diffusion in white matter tracts is often directionally dependent or anisotropic because of the ordered structure of axons and myelin sheaths. Radiation-induced white matter damage may be evaluated noninvasively using DTI, which has a long history of supporting evidence as an imaging biomarker [6–10].

DTI assesses water molecule diffusion in the brain, which changes with white matter fiber direction, density, and myelination. Mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) are three related values of this measurement. These indices are related to the magnitudes of diffusion that are perpendicular and parallel to white matter fibers, respectively. The fractional anisotropy (FA) index is another kind of diffusion index that is often employed. It is a normalized value that may vary from zero (which indicates equal diffusion in all directions) to one (diffusion along a single axis only). FA is a measure of the overall density and integrity of the brain’s white matter; a reduction in FA has been linked to a wide variety of brain disorders [7, 11–17].

Radiation therapy (RT) for primary brain tumors and brain metastases from extracranial tumors is performed annually on hundreds of thousands of patients around the world [18–23]. There are two types of brain radiotherapy: whole brain (WBRT) and partial brain (PBRT). WBRT involves irradiating the whole brain and brainstem, whereas PBRT involves irradiating the tumor or tumor bed and surrounding margin and some healthy brain tissue [21, 24, 25]. Stereotactic radiosurgery (SRS) uses accurate 3D imaging and localization to deliver ablative doses of radiation to the tumor while exposing healthy brain tissue to a minimum [23, 25].

RT may cause post-treatment neurocognitive deterioration, with verbal and visuospatial memory being the most commonly reported. Neurocognitive decline has been an independently associated predictor of survival in individuals with brain malignancies, and the long-term consequences of RT are usually permanent and gradual [12, 26]. Damage to white matter (WM) pathways, vascular injury, and neuroinflammation are all factors that contribute to radiation-induced brain damage. Axonal degeneration and demyelination of WM have been shown in histopathological investigations following radiation exposure, and diffusion tensor imaging (DTI) biomarkers are related to these alterations [12, 27, 28].

Based on our research, the aim of the study is to collect and classify brain diffusion MRI biomarkers after chemoradiotherapy.

Materials and methods

Search strategy

On November 12th, 2021, the search for articles was started, and on July 3rd, 2022, it was completed. Diffusion MRI, Brain, Chemoradiotherapy, Imaging Biomarker, and Neuroimaging were among the keywords used in the search, which were entered into the following template in the PubMed electronic database and the Google Scholar search engine.

Inclusion criteria (refer to DWI biomarkers) were as follows:

  • English-language original articles;
  • original and review studies looked at DWI biomarkers after brain chemoradiotherapy and used MRI data;
  • original research that looked at long-term cognitive and behavioral disorders.
  • Exclusion criteria were as follows:
  • all of the articles are written in languages other than English;
  • case studies and short reports;
  • the studies did not employ an MRI or any other imaging modality (particularly in cases of neurological manifestations).
Literature screening

Approximately 100 publications were discovered during the first search, which comprised original studies, review articles, case reports, and short reports. As a result, case studies and short reports were excluded, but the references in the literature review were examined. After the final evaluation, 32 original papers and 6 review articles remained based on the inclusion and exclusion criteria. Biomarkers and long-term cognitive-behavioral disorders were comprehensively retrieved from all of the papers in the reference list.

The following parameters were considered throughout the search:

  • first author;
  • the date of publication;
  • using MRI.

Finally, after doing database searches and collecting publications, they were divided into three categories: white matter changes, radiation necrosis, and neurocognitive damages.

MRI
Devices

Various investigations have employed devices of varying field strengths and commercial models to study changes in diffusion parameters in brain tissue in relation to necrotic and neurocognitive damage. The types of these devices include a 3.0T system (Philips Medical Systems, Best, the Netherlands), a 3.0T system (Achieva, Philips, Eindhoven, The Netherlands), a 3.0T 750, and 1.5 T and Signa Excite HDx scanner (General Electric Healthcare, Milwaukee, Wisconsin, United States), a Signa 1.5T and 3.0T scanner (General Electric Healthcare, Chicago, IL, United States), a Sonata 1.5T scanner (Siemens Healthcare, Erlangen, Germany), a TimeTrio 3.0T scanner (Siemens Medical Solutions, Malvern, PA, USA), and a 3.0T scanner (Trio MAGNETOM; Siemens Healthcare, Erlangen, Germany) cases. It is important to note that in some experiments, just one or even three types of a device were employed.

Diffusion-weighted techniques
DWI

DWI is a potential MRI technique for characterizing the response to RT and the damage to normal tissue. Changes in the mobility of water molecules in tissue are reflected in the MR signal in DWI. Brownian motion, as it is often referred to, is the result of heat agitation and is strongly impacted by the water’s cellular structure. Neurosurgical evaluations of brain tumors may greatly benefit from DWI. One of the most commonly used parameters derived from DWI is the apparent diffusion coefficient (ADC), which quantifies the magnitude of water diffusion in tissue. ADC can provide valuable information about tumor cellularity, necrosis, edema, and perfusion, which can help in diagnosis, prognosis, treatment planning, and monitoring of brain tumors. ADC can also detect early changes in tissue microstructure after RT, which can indicate the efficacy of treatment and the risk of complications. Therefore, ADC is an important biomarker for assessing brain tumors and their response to RT [28, 29].

DTI

The advanced DTI technique is a helpful tool for measuring the damage to white matter that is caused by radiation. It is able to detect abnormalities much earlier than conventional imaging approaches. It is feasible to use the DTI’s capacity to identify white matter degradation in order to determine whether or not RT has varied detrimental effects on various parts of the brain [29, 30].

We selected MD, RD, AD, and FA as biomarkers because they capture different aspects of white matter microstructure and integrity that can be altered by brain disorders. MD reflects the average diffusion of water molecules in the brain tissue, which can be affected by factors such as cell density, membrane permeability, and extracellular space. RD reflects the diffusion of water molecules orthogonal to the main fiber direction, which can be indicative of demyelination or axonal loss. AD reflects the diffusion of water molecules along the main fiber direction, which can be suggestive of axonal damage or degeneration. FA reflects the degree of anisotropy or directionality of water diffusion in the brain tissue, which can be associated with fiber coherence, organization, and alignment. These parameters have been widely used and validated in previous studies of various brain disorders, and they provide complementary information about the structural changes in white matter that may underlie the pathophysiology of these disorders. We did not use other parameters, such as mode of anisotropy or trace of the diffusion tensor, because they are less commonly used and less informative than the ones we selected [12, 27, 28].

Chemoradiation therapy techniques
Chemotherapy

Chemotherapy medications may be used after surgery, in conjunction with radiotherapy, in cases of recurrence of the disease, or even as a substitute for radiation treatment in children, depending on the patient’s health. Brain tumors cannot be effectively treated with chemotherapy alone because of the blood-brain barrier (BBB) [31, 32].

External radiotherapy

Based on the type and location of the lesion, different radiotherapy techniques are used to treat brain tumors. For the most precise RT treatment, stereotactic radiosurgery (SRS) makes use of three-dimensional (3D) imaging to locate and treat brain malignancies in a single session. Some SRS techniques include the X-ray knife and the Gamma-knife [33–35].

Other methods of external radiotherapy include delivering the tumor from the outside in numerous doses. Three-dimensional conformal radiation therapy (3D-CRT) reliably identifies the planning target volume (PTV) and adjacent organs at risk (OARs) using 3D imaging [36]. In order to optimize the radiation flux profile, novel modulation systems, named intensity modulated radiation therapy (IMRT), computer-controlled multi-leaf dynamic collimators, and methodologies such as inverse planning are required to apply this strategy [37, 38] .The most recent versions include rotating cone beams as therapy with multiple arcs at a consistent dose rate in each different sub-field of radiation or volumetric modulated arc therapies (VMAT) as treatment with rotating cone beam radiation with varying shapes and radiation intensities [39, 40].

Results and Discussion

Brain diffusion MRI biomarkers
White matter changes

Neuron myelinated fibers, also known as tracts, are found in white matter (WM), the deepest component of the brain tissue in the central nervous system. The white matter tracts of the corpus callosum and the internal capsules are crucial [41]. RT for various types of brain tumors, such as gliomas, medulloblastomas, and meningiomas, will always lead to alterations in the tumor’s volume and the ratio of intracellular to extracellular volumes [42–44]. DTI and DWI, by using intrinsic tissue properties, offer a helpful quantitative evaluation of tissue structure, particularly myelinated fiber bundles in WM [45, 46].

Radiation necrosis

Focal neurological impairments are often associated with radiation necrosis, which affects mostly the white matter and is generally permanent and progressive [47]. According to the structure of the nerve fiber axons and the myelin sheath, the flow of water molecules along the length of the nerve fiber is greater than in other directions. Due to the existence of numerous membranes, restricted space, and high viscosity, the quantity of movement of water molecules in the intracellular space is smaller than that in the extracellular environment. As a result, since radiation affects the ratio of intracellular to extracellular volumes, diffusion imaging biomarkers are very useful to assess radiation damage. Utilizing these biomarkers, like other MR imaging procedures, is non-invasive and does not require any further interventions. White matter is particularly vulnerable to radiation damage because of the way water molecules move through the tissue [48]. White matter axial and radial diffusivity changes are often interpreted as indicators of axonal injury or demyelination [49]. After beginning RT, an imaging biomarker might be used to determine the radiation sensitivity of an individual’s brain normal tissue [50].

Neurocognitive damages

Neurocognitive abnormalities are clearly linked to radiation treatment and are an important adverse effect of life-saving interventions in youngsters [51]. After irradiation, cognitive loss may begin to show up months or years later and worsen with time [52]. IMRT, stereotactic radiosurgery, intracranial brachytherapy, and restricted fraction size may minimize normal tissue damage [53]. Some neuropsychological deficiencies (such as a lack of ability to recall information or spatially interpret information) still persist [54, 55].

Table 1 provides the findings that relate to alterations in diffusion biomarkers in WM changes, radiation necrosis, and neurocognitive damage. As well, Table 2 is a representation of the common alterations that have occurred in the most significant MR diffusion biomarkers, including FA, MD, RD, AD, and ADC.

Table 1. Studies of diffusion biomarker assessment after radiation damage

First author [year]

Patient numbers

Imaging technique(s)

Max. directions/b-values [s/mm2]

Radiotherapy technique(s)

Total dose/fraction size [Gy]

Chemotherapy

Imaging biomarker(s)

Brain tumor(s)

Changes in imaging biomarker(s)

White matter changes

Chakhoyan (2018) [56]

23

DWI

NA/0, 50, 100, 250, 500, 750, 1000, 2500, 3500 and 5000

3D-CRT

60/2

Temozolomide

ADC

Glioblastoma

No difference in diffusion biomarkers change in NAWM between pre- and post-chemoradiation

Nagesh (2008) [6]

25

DTI

9/0 and 1000

3D-CRT

5081/1.82.7

Temozolomide

FA
MD
AD
RD

Cerebral tumors

FA decreased, and MD, RD, and AD increased in the genu and splenium

Hope (2015) [57]

18

DTI

15/0 and 800

3D-CRT

60/2

Temozolomide

FA
MD
AD
RD

HGGs

In FA, no significant time evolution was observed, and there was increased MD, RD, and AD in NAWM

Haris (2008) [58]

5

DTI

10/0 and 1000

3D-CRT

54/1.8

Temozolomide

FA
MD

LGGs

FA decreased and MD increased in NAWM

Tringale (2019) [59]

54

DTI

15/0, 500, 1500, and 4000

Proton or photons RT

50.460/1.82

NA

FA
MD
RD
AD

Primary brain tumor

whereas FA decreased in the right caudal anterior cingulate, MD, RD increased bilaterally, whereas no significant changes in AD were found during this time-period

Chapman (2012) [60]

10

DTI

9/0 and 1000

3D-CRT

50.459.4/1.8

NA

FA
AD
RD

Benign tumors

While FA was used for volume adjustment, it was not included in the analysis. Following RT, AD decreased and RD increased

Connor (2016) [61]

32

DTI

15/0, 500, 1500, and 4000

EBRT

60/2

Chemotherapy

FA
MD
AD
RD

HGGs

MD, AD, and RD increased significantly with time and dose, and a corresponding decrease in FA

Chang (2014) [62]

15

DWI

DTI

6/0 and 1000

Partial brain irradiation or SRS

1825

NA

ADC
FA

Malignant gliomas

ADC increased (receiving more than 5 Gy) and decreased (more than 12 Gy) after 7 days and 2 months. FA decreased more after 2 months

Tibbs (2020) [63]

44

DTI

15/0, 500, 1500, and 4000

Proton therapy and IMRT or VMAT

50.470/1.82

NA

FA
MD

Primary brain tumor

There was decreased FA in the left arcuate fasciculus, ILF, and IFOF. Increased MD in all of the left-sided white matter tracts, left arcuate fasciculus, ILF, IFOF

Qiu (2007) [64]

22

DTI

25/0 and 1200

3D-CRT

23.440/1.82

Chemotherapy

FA

Medulloblastoma

Decreased FA in the frontal lobe and parietal lobe white matter, and the frontal lobe having a significantly larger difference in FA compared with the parietal lobe

Connor (2017) [48]

49

DTI

15/0, 500, 1500, and 4000

3D-CRT

60/2

NA

FA
MD
AD
RD

Primary brain tumors

Decreases in FA connote white matter disruption. For MD, the column and body of fornix, cingulum bundle, tapetum, and genu and body of the corpus callosum were among the ROIs to show the most dose sensitivity

Zhu (2016) [65]

33

DTI

20/0, 800, and 1000

EBRT

50.470/2

NA

AD
RD

Low-grade or benign brain tumors

There was a dose-dependent progressive decrease in AD over time after RT. RD was significantly related to maximum doses received

Ding (2017) [66]

87

DTI

NA/0 and 1000

2D-CRT or IMRT

5076/2

Chemotherapy

FA

Nasopharyngeal carcinoma patients with normal-appearing brains

Within an FA mask in the putative white matter, there was a significant reduction in the FA value. Specifically, after 12 months of follow ups from the completion of RT, the FA in the bilateral splenium of the corpus callosum was reduced compared to the pre‐RT level

Hua (2012) [10]

109 DTI studies (from 20 brain tumor patients)

DTI

12/0 and 1000

3D-CRT

23.4 Gy or 3639.6 Gy/1.8

Chemotherapy

FA

Medulloblastomas, supratentorial primitive neuroectodermal tumors, atypical teratoid rhabdoid tumors, and HGGs

Decreased FA

Raschke (2019) [67]

22

DTI

32/0 and 1000

Proton or photon therapy

13.6

Chemotherapy

MD
RD
AD
FA

HGGs

Significant reductions in MD, RD, and AD and an increase in FA

Chapman (2013) [30]

14

DTI

15/0 and 1000

3D-CRT

30 and 37.5/3 and 2.5

Chemotherapy

FA
RD
AD

Brain white matter structures

Significant FA decreases and RD increases. There were no significant changes in AD between pre-RT and end-RT

Huynh-Le (2021) [68]

44

DTI

15/0, 500, 1500, and 4000

Proton therapy, IMRT, and VMAT

50.470/1.82

NA

MD
FA

Primary brain tumors

Reduction in FA and an increase in MD

Sahin (2021) [69]

17

DTI

7/0 and 1000

EBRT

60/2

Chemotherapy

FA

Glioblastoma

FA decrease

Cho (2020) [70]

40

DWI

NA/0 and 1000

EBRT

NA

Chemotherapy

ADC

Glioblastoma

The ipsilesional SVZ had lower ADC values compared to the contralesional SVZ before treatment, as ADC values of the ipsilesional SVZ increased

Khong (2003) [71]

9

DTI

25/0 and 1200

3D-CRT

30.640 and 50.454/1.82

NA

FA

Medulloblastoma

Significant reduction of FA was seen in all anatomic sites in the patient group compared with FA in control subjects, except in the frontal periventricular WM.

Mabbott (2006) [72]

8

DWI

DTI

25/0 and 1000

3D-CRT

3636.6 and 23.4/NA

Either etoposide/cisplatin/cyclophosphamide/vincristine or CCNU/vincristine/cisplatin

ADC
FA

Medulloblastoma

Overall, mean FA was lower and ADC was higher in the radiated group relative to controls

Ravn (2013) [29]

19

DWI

32/0 and 1300

3D-CRT

4559.4/1.8

NA

ADC

Astrocytoma, pituitary adenoma, meningioma, and craniopharyngioma

ADC increase

Khong (2006) [73]

20

DTI

25/0 and 1250

3D-CRT

5055.8/NA

Chemotherapy

FA

Childhood MED and ALL

FA increase

Makola (2017) [74]

22

DTI

25/0 and 1000

EBRT

4559.4/NA

With or without chemotherapy

FA
RD

A pediatric brain tumor

The FA and RD did not change significantly

Prust (2015) [75]

14

DWI

NA

3D-CRT

60/2

Chemotherapy

ADC

Glioblastoma

ADC increased within the subventricular zone

Radiation necrosis

Nazem-Zadeh (2014) [76]

29

DTI

9/0 and 1000

3D-CRT

60 and 6681/2 and 2.52.6

Temozolomide

RD

Glioblastoma

RD increase

Liu (2018) [80]

43

DWI

NA/0 and 1000

EBRT

4050/NA

Chemotherapy

ADC

Brain metastases from lung cancer

ADC values significantly increased after both one and two treatment cycles. In effective group, the ADC values were significantly increased after one and two treatment cycles. While, there are no difference in invalid group after one treatment cycle but decreased after two treatment cycles

Feng (2022) [81]

46

DWI

20/0 and 1000

EBRT

NA

Surgical intervention followed by chemoradiotherapy

ADC

Glioblastoma

Significant differences between the tumor recurrence from radiation necrosis groups in terms of ADC

Neurocognitive damages

Tringale (2019) [59]

54

DTI

15/0, 500,

1500, and 4000

Proton or photons RT

50.460/1.82

Chemotherapy

FA
MD
RD
AD

Glioma and non-glioma

There were decreases in FA and increases in MD in the CAC at 3-months post-RT. CAC changes were characterized by increased RD bilaterally. AD did not change significantly

Chapman (2012) [60]

10

DTI

9/0 and 1000

3D-CRT

50.459.4/1.8

NA

AD
RD

Low-grade or benign tumors

Following RT, AD decreased and RD increased

Chapman (2016) [77]

27

DTI

20/0 and 1000

3D-CRTand IMRT

50.470/2

15% had concurrent chemotherapy with temozolomide

RD
AD

Benign or low-grade tumors

Decreasing AD and increasing RD during RT

Bian (2018) [78]

23

DTI

32/0 and 1000

IMRT

54 and 60/1.8 and 2

Temozolomid

FA

HGGs

FA in the contralateral hippocampus decreased at 6 and 9 months after radiotherapy. FA in the ipsilateral hippocampus before radiochemotherapy decreased compared with 6 months after radiotherapy

Tringale (2019) [12]

27

DTI

15/0, 500,

1500, and 4000

IMRT and proton therapy

50.460/1.8-2

Chemotherapy

FA
MD

Primary brain tumor

Decreasing FA and increasing MD

Law (2011) [79]

67

DTI

31/0 and 1000

3D-CRT

23.436/NA

Chemotherapy

FA
RD
MD

Posterior fossa tumor

All imaging biomarkers have not changed significantly

Table 2. Magnetic resonance (MR) diffusion biomarkers changes.

Radiation damages

MR diffusion biomarker changes

FA

MD

RD

AD

ADC

White matter changes

155148.png

155160.png

155165.png

155170.png

Dependent on radiation dose

Radiation necrosis

N/A

N/A

155194.png

N/A

N/A

Naurocognitive damages

155175.png

155180.png

155189.png

N/A

N/A

Conclusion

Neuroimaging biomarkers after chemoradiotherapy were evaluated using diffusion imaging methods (DWI and DTI). We found that biomarkers change depending on the degree of tissue damage. Some studies demonstrate that biomarker alterations are increasing, while others show that they are decreasing. As a consequence, there is disagreement over the general pattern of change. Even so, FA changes are predicted to decrease, whereas MD and RD changes are expected to increase. It is proposed that further longitudinal studies be conducted to determine the effectiveness of diffusion imaging biomarkers.

Conflict of interest

The authors declare no financial or other conflicts of interest.

Funding

No funding.

Author’s Contributions

S.G. and M.M. contributed to the conception and design of the study; Sh.B., F.M., M.Z., and Gh.T. contributed to the data collection. S.G. and M.M. contributed to drafting the text and preparing the Table 2.

Ethical statement

No human or animal subjects were used in the research.

References

  1. Sugahara T, Korogi Y, Kochi M, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999; 9(1): 53–60, doi: 10.1002/(sici)1522-2586(199901)9:1<53::aid-jmri7>3.0.co;2-2, indexed in Pubmed: 10030650.
  2. Provenzale JM, Mukundan S, Barboriak DP. Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology. 2006; 239(3): 632–649, doi: 10.1148/radiol.2393042031, indexed in Pubmed: 16714455.
  3. Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med. 2000; 43(6): 828–836, doi: 10.1002/1522-2594(200006)43:6<828::aid-mrm8>3.0.co;2-p, indexed in Pubmed: 10861877.
  4. Ellingson BM, Malkin MG, Rand SD, et al. Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging. 2010; 31(3): 538–548, doi: 10.1002/jmri.22068, indexed in Pubmed: 20187195.
  5. Chenevert TL, Stegman LD, Taylor JM, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000; 92(24): 2029–2036, doi: 10.1093/jnci/92.24.2029, indexed in Pubmed: 11121466.
  6. Nagesh V, Tsien CI, Chenevert TL, et al. Radiation-induced changes in normal-appearing white matter in patients with cerebral tumors: a diffusion tensor imaging study. Int J Radiat Oncol Biol Phys. 2008; 70(4): 1002–1010, doi: 10.1016/j.ijrobp.2007.08.020, indexed in Pubmed: 18313524.
  7. Ye-Ting Z, Wang GS, Chen XD, et al. MRI Finding in Delayed Extensive Brain Lesions after Radiation Therapy :Cortical Laminar Necrosis and White Matter Myelinolysis. Adv Mol Imag. 2012; 02(01): 1–3, doi: 10.4236/ami.2012.21001.
  8. Chan KC, Khong PL, Cheung MM, et al. MRI of late microstructural and metabolic alterations in radiation-induced brain injuries. J Magn Reson Imaging. 2009; 29(5): 1013–1020, doi: 10.1002/jmri.21736, indexed in Pubmed: 19388094.
  9. Sundgren PC, Cao Y, Sundgren PC, et al. Metabolic alterations: a biomarker for radiation-induced normal brain injury-an MR spectroscopy study. J Magn Reson Imaging. 2009; 29(2): 291–297, doi: 10.1002/jmri.21657, indexed in Pubmed: 19161192.
  10. Hua C, Merchant TE, Gajjar A, et al. Brain tumor therapy-induced changes in normal-appearing brainstem measured with longitudinal diffusion tensor imaging. Int J Radiat Oncol Biol Phys. 2012; 82(5): 2047–2054, doi: 10.1016/j.ijrobp.2011.03.057, indexed in Pubmed: 21664060.
  11. Li Z, Peck KK, Brennan NP, et al. Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models. J Biomed Sci Eng. 2013; 6(2): 192–200, doi: 10.4236/jbise.2013.62023, indexed in Pubmed: 25328583.
  12. Tringale KR, Nguyen TT, Karunamuni R, et al. Quantitative Imaging Biomarkers of Damage to Critical Memory Regions Are Associated With Post-Radiation Therapy Memory Performance in Brain Tumor Patients. Int J Radiat Oncol Biol Phys. 2019; 105(4): 773–783, doi: 10.1016/j.ijrobp.2019.08.003, indexed in Pubmed: 31408667.
  13. Hancock C, Bernal B, Medina C, et al. Cost Analysis of Diffusion Tensor Imaging and MR Tractography of the Brain. Open J Radiol. 2014; 04(03): 260–269, doi: 10.4236/ojrad.2014.43034.
  14. Mac Donald CL, Dikranian K, Bayly P, et al. Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J Neurosci. 2007; 27(44): 11869–11876, doi: 10.1523/JNEUROSCI.3647-07.2007, indexed in Pubmed: 17978027.
  15. Song SK, Sun SW, Ramsbottom MJ, et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002; 17(3): 1429–1436, doi: 10.1006/nimg.2002.1267, indexed in Pubmed: 12414282.
  16. Grigoriadis N, Ben-Hur T, Karussis D, et al. Axonal damage in multiple sclerosis: a complex issue in a complex disease. Clin Neurol Neurosurg. 2004; 106(3): 211–217, doi: 10.1016/j.clineuro.2004.02.017, indexed in Pubmed: 15177770.
  17. Sun SW, Liang HF, Cross AH, et al. Evolving Wallerian degeneration after transient retinal ischemia in mice characterized by diffusion tensor imaging. Neuroimage. 2008; 40(1): 1–10, doi: 10.1016/j.neuroimage.2007.11.049, indexed in Pubmed: 18187343.
  18. Chi A, Komaki R. Treatment of brain metastasis from lung cancer. Cancers (Basel). 2010; 2(4): 2100–2137, doi: 10.3390/cancers2042100, indexed in Pubmed: 24281220.
  19. Shi L, Olson J, D’Agostino R, et al. Aging masks detection of radiation-induced brain injury. Brain Res. 2011; 1385: 307–316, doi: 10.1016/j.brainres.2011.02.034, indexed in Pubmed: 21338580.
  20. Greene-Schloesser D, Moore E, Robbins ME. Molecular pathways: radiation-induced cognitive impairment. Clin Cancer Res. 2013; 19(9): 2294–2300, doi: 10.1158/1078-0432.CCR-11-2903, indexed in Pubmed: 23388505.
  21. Moore ED, Kooshki M, Wheeler KT, et al. Differential expression of Homer1a in the hippocampus and cortex likely plays a role in radiation-induced brain injury. Radiat Res. 2014; 181(1): 21–32, doi: 10.1667/RR13475.1, indexed in Pubmed: 24377717.
  22. Wu PH, Coultrap S, Pinnix C, et al. Radiation induces acute alterations in neuronal function. PLoS One. 2012; 7(5): e37677, doi: 10.1371/journal.pone.0037677, indexed in Pubmed: 22662188.
  23. Makale MT, McDonald CR, Hattangadi-Gluth J, et al. Brain irradiation and long-term cognitive disability: Current concepts. Nature Rev Neurol. 2017; 13(1): 52.
  24. Jenrow KA, Brown SL, Lapanowski K, et al. Selective inhibition of microglia-mediated neuroinflammation mitigates radiation-induced cognitive impairment. Radiat Res. 2013; 179(5): 549–556, doi: 10.1667/RR3026.1, indexed in Pubmed: 23560629.
  25. Lippitz B, Lindquist C, Paddick I, et al. Stereotactic radiosurgery in the treatment of brain metastases: the current evidence. Cancer Treat Rev. 2014; 40(1): 48–59, doi: 10.1016/j.ctrv.2013.05.002, indexed in Pubmed: 23810288.
  26. Greene-Schloesser D, Robbins ME. Radiation-induced cognitive impairment--from bench to bedside. Neuro Oncol. 2012; 14 Suppl 4(Suppl 4): iv37–iv44, doi: 10.1093/neuonc/nos196, indexed in Pubmed: 23095829.
  27. Panagiotakos G, Alshamy G, Chan B, et al. Long-term impact of radiation on the stem cell and oligodendrocyte precursors in the brain. PLoS One. 2007; 2(7): e588, doi: 10.1371/journal.pone.0000588, indexed in Pubmed: 17622341.
  28. Hung P, Tohyama S, Zhang JY, et al. Temporal disconnection between pain relief and trigeminal nerve microstructural changes after Gamma Knife radiosurgery for trigeminal neuralgia. J Neurosurg. 2019 [Epub ahead of print]: 1–9, doi: 10.3171/2019.4.JNS19380, indexed in Pubmed: 31299654.
  29. Ravn S, Holmberg M, Sørensen P, et al. Differences in supratentorial white matter diffusion after radiotherapy--new biomarker of normal brain tissue damage? Acta Oncol. 2013; 52(7): 1314–1319, doi: 10.3109/0284186X.2013.812797, indexed in Pubmed: 23981047.
  30. Chapman CH, Nazem-Zadeh M, Lee OE, et al. Regional variation in brain white matter diffusion index changes following chemoradiotherapy: a prospective study using tract-based spatial statistics. PLoS One. 2013; 8(3): e57768, doi: 10.1371/journal.pone.0057768, indexed in Pubmed: 23469234.
  31. Arvanitis CD, Ferraro GB, Jain RK. The blood-brain barrier and blood-tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020; 20(1): 26–41, doi: 10.1038/s41568-019-0205-x, indexed in Pubmed: 31601988.
  32. Patel JP, Spiller SE, Barker ED. Drug penetration in pediatric brain tumors: Challenges and opportunities. Pediatr Blood Cancer. 2021; 68(6): e28983, doi: 10.1002/pbc.28983, indexed in Pubmed: 33719183.
  33. Redmond KJ, Gui C, Benedict S, et al. Tumor Control Probability of Radiosurgery and Fractionated Stereotactic Radiosurgery for Brain Metastases. Int J Radiat Oncol Biol Phys. 2021; 110(1): 53–67, doi: 10.1016/j.ijrobp.2020.10.034, indexed in Pubmed: 33390244.
  34. Shah JL, Li G, Shaffer JL, et al. Stereotactic Radiosurgery and Hypofractionated Radiotherapy for Glioblastoma. Neurosurgery. 2018; 82(1): 24–34, doi: 10.1093/neuros/nyx115, indexed in Pubmed: 28605463.
  35. Leksell L. The stereotactic method and radiosurgery of the brain. Acta Chir Scand. 1951; 102: 316–9.
  36. Pelizzari CA, Chen GT, Spelbring DR, et al. Accurate three-dimensional registration of CT, PET, and/or MR images of the brain. J Comput Assist Tomogr. 1989; 13(1): 20–26, doi: 10.1097/00004728-198901000-00004, indexed in Pubmed: 2492038.
  37. Bortfeld T. IMRT: a review and preview. Phys Med Biol. 2006; 51(13): R363–R379, doi: 10.1088/0031-9155/51/13/r21.
  38. Cho B, Cho B. Intensity-modulated radiation therapy: a review with a physics perspective. Radiat Oncol J. 2018; 36(1): 1–10, doi: 10.3857/roj.2018.00122, indexed in Pubmed: 29621869.
  39. Lafond C, Jouyaux F, Bellec J, et al. [Which IMRT? From “step and shoot” to VMAT: physicist point of view]. Cancer Radiother. 2010; 14(6-7): 539–549, doi: 10.1016/j.canrad.2010.06.012, indexed in Pubmed: 20728394.
  40. Muzumder S, John Sebastian MG. Plan Evaluation in IMRT and VMAT. In: Mallick S, Rath GK, Benson R. ed. Practical Radiation Oncology. Springer, Singapore 2020: 151–156.
  41. Buyanova IS, Arsalidou M. Cerebral White Matter Myelination and Relations to Age, Gender, and Cognition: A Selective Review. Front Hum Neurosci. 2021; 15: 662031, doi: 10.3389/fnhum.2021.662031, indexed in Pubmed: 34295229.
  42. Barani IJ, Larson DA. Radiation therapy of glioblastoma. Cancer Treat Res. 2015; 163: 49–73, doi: 10.1007/978-3-319-12048-5_4, indexed in Pubmed: 25468225.
  43. Balducci M, Chiesa S, Chieffo D, et al. The role of radiotherapy in adult medulloblastoma: long-term single-institution experience and a review of the literature. J Neurooncol. 2012; 106(2): 315–323, doi: 10.1007/s11060-011-0665-7, indexed in Pubmed: 21805324.
  44. Pinzi V, Bisogno I, Prada F, et al. Radiotherapy of meningioma: a treatment in need of radiobiological research. Int J Radiat Biol. 2018; 94(7): 621–627, doi: 10.1080/09553002.2018.1478157, indexed in Pubmed: 29775403.
  45. Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001; 13(4): 534–546, doi: 10.1002/jmri.1076, indexed in Pubmed: 11276097.
  46. Johansen-Berg H. Behavioural relevance of variation in white matter microstructure. Curr Opin Neurol. 2010; 23(4): 351–358, doi: 10.1097/WCO.0b013e32833b7631, indexed in Pubmed: 20581685.
  47. Fink J, Born D, Chamberlain MC. Radiation necrosis: relevance with respect to treatment of primary and secondary brain tumors. Curr Neurol Neurosci Rep. 2012; 12(3): 276–285, doi: 10.1007/s11910-012-0258-7, indexed in Pubmed: 22350279.
  48. Connor M, Karunamuni R, McDonald C, et al. Regional susceptibility to dose-dependent white matter damage after brain radiotherapy. Radiother Oncol. 2017; 123(2): 209–217, doi: 10.1016/j.radonc.2017.04.006, indexed in Pubmed: 28460824.
  49. Song SK, Sun SW, Ju WK, et al. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003; 20(3): 1714–1722, doi: 10.1016/j.neuroimage.2003.07.005, indexed in Pubmed: 14642481.
  50. Jeraj R, Cao Y, Ten Haken RK, et al. Imaging for assessment of radiation-induced normal tissue effects. Int J Radiat Oncol Biol Phys. 2010; 76(3 Suppl): S140–S144, doi: 10.1016/j.ijrobp.2009.08.077, indexed in Pubmed: 20171509.
  51. Askins MA, Moore BD. Preventing neurocognitive late effects in childhood cancer survivors. J Child Neurol. 2008; 23(10): 1160–1171, doi: 10.1177/0883073808321065, indexed in Pubmed: 18952582.
  52. Monje M, Dietrich J. Cognitive side effects of cancer therapy demonstrate a functional role for adult neurogenesis. Behav Brain Res. 2012; 227(2): 376–379, doi: 10.1016/j.bbr.2011.05.012, indexed in Pubmed: 21621557.
  53. Baskar R, Lee KA, Yeo R, et al. Cancer and radiation therapy: current advances and future directions. Int J Med Sci. 2012; 9(3): 193–199, doi: 10.7150/ijms.3635, indexed in Pubmed: 22408567.
  54. Pazzaglia S, Briganti G, Mancuso M, et al. Neurocognitive Decline Following Radiotherapy: Mechanisms and Therapeutic Implications. Cancers (Basel). 2020; 12(1), doi: 10.3390/cancers12010146, indexed in Pubmed: 31936195.
  55. Michaelidesová A, Konířová J, Bartůněk P, et al. Differentiation Induction as a Response to Irradiation in Neural Stem Cells In Vitro. Cancers (Basel). 2019; 11(7), doi: 10.3390/cancers11070913, indexed in Pubmed: 31261863.
  56. Chakhoyan A, Woodworth DC, Harris RJ, et al. Mono-exponential, diffusion kurtosis and stretched exponential diffusion MR imaging response to chemoradiation in newly diagnosed glioblastoma. J Neurooncol. 2018; 139(3): 651–659, doi: 10.1007/s11060-018-2910-9, indexed in Pubmed: 29855771.
  57. Hope TR, Vardal J, Bjørnerud A, et al. Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients. J Magn Reson Imaging. 2015; 41(2): 414–423, doi: 10.1002/jmri.24533, indexed in Pubmed: 24399480.
  58. Haris M, Kumar S, Raj MK, et al. Serial diffusion tensor imaging to characterize radiation-induced changes in normal-appearing white matter following radiotherapy in patients with adult low-grade gliomas. Radiat Med. 2008; 26(3): 140–150, doi: 10.1007/s11604-007-0209-4, indexed in Pubmed: 18683569.
  59. Tringale KR, Nguyen T, Bahrami N, et al. Identifying early diffusion imaging biomarkers of regional white matter injury as indicators of executive function decline following brain radiotherapy: A prospective clinical trial in primary brain tumor patients. Radiother Oncol. 2019; 132: 27–33, doi: 10.1016/j.radonc.2018.11.018, indexed in Pubmed: 30825966.
  60. Chapman CH, Nagesh V, Sundgren PC, et al. Diffusion tensor imaging of normal-appearing white matter as biomarker for radiation-induced late delayed cognitive decline. Int J Radiat Oncol Biol Phys. 2012; 82(5): 2033–2040, doi: 10.1016/j.ijrobp.2011.01.068, indexed in Pubmed: 21570218.
  61. Connor M, Karunamuni R, McDonald C, et al. Dose-dependent white matter damage after brain radiotherapy. Radiother Oncol. 2016; 121(2): 209–216, doi: 10.1016/j.radonc.2016.10.003, indexed in Pubmed: 27776747.
  62. Chang Z, Kirkpatrick JP, Wang Z, et al. Evaluating radiation-induced white matter changes in patients treated with stereotactic radiosurgery using diffusion tensor imaging: a pilot study. Technol Cancer Res Treat. 2014; 13(1): 21–28, doi: 10.7785/tcrt.2012.500358, indexed in Pubmed: 23862743.
  63. Tibbs MD, Huynh-Le MP, Karunamuni R, et al. Microstructural Injury to Left-Sided Perisylvian White Matter Predicts Language Decline After Brain Radiation Therapy. Int J Radiat Oncol Biol Phys. 2020; 108(5): 1218–1228, doi: 10.1016/j.ijrobp.2020.07.032, indexed in Pubmed: 32712255.
  64. Qiu D, Kwong DLW, Chan GCF, et al. Diffusion tensor magnetic resonance imaging finding of discrepant fractional anisotropy between the frontal and parietal lobes after whole-brain irradiation in childhood medulloblastoma survivors: reflection of regional white matter radiosensitivity? Int J Radiat Oncol Biol Phys. 2007; 69(3): 846–851, doi: 10.1016/j.ijrobp.2007.04.041, indexed in Pubmed: 17544593.
  65. Zhu T, Chapman CH, Tsien C, et al. Effect of the Maximum Dose on White Matter Fiber Bundles Using Longitudinal Diffusion Tensor Imaging. Int J Radiat Oncol Biol Phys. 2016; 96(3): 696–705, doi: 10.1016/j.ijrobp.2016.07.010, indexed in Pubmed: 27681767.
  66. Ding Z, Zhang H, Lv XF, et al. Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction. Hum Brain Mapp. 2018; 39(1): 407–427, doi: 10.1002/hbm.23852, indexed in Pubmed: 29058342.
  67. Raschke F, Wesemann T, Wahl H, et al. Reduced diffusion in normal appearing white matter of glioma patients following radio(chemo)therapy. Radiother Oncol. 2019; 140: 110–115, doi: 10.1016/j.radonc.2019.06.022, indexed in Pubmed: 31265941.
  68. Huynh-Le MP, Tibbs MD, Karunamuni R, et al. Microstructural Injury to Corpus Callosum and Intrahemispheric White Matter Tracts Correlate With Attention and Processing Speed Decline After Brain Radiation. Int J Radiat Oncol Biol Phys. 2021; 110(2): 337–347, doi: 10.1016/j.ijrobp.2020.12.046, indexed in Pubmed: 33412257.
  69. Şahin S, Ertekin E, Şahin T, et al. Evaluation of normal-appearing white matter with perfusion and diffusion MRI in patients with treated glioblastoma. MAGMA. 2022; 35(1): 153–162, doi: 10.1007/s10334-021-00990-5, indexed in Pubmed: 34951690.
  70. Cho N, Wang C, Raymond C, et al. Diffusion MRI changes in the anterior subventricular zone following chemoradiation in glioblastoma with posterior ventricular involvement. J Neurooncol. 2020; 147(3): 643–652, doi: 10.1007/s11060-020-03460-5, indexed in Pubmed: 32239430.
  71. Khong PL, Kwong DLW, Chan GCF, et al. Diffusion-tensor imaging for the detection and quantification of treatment-induced white matter injury in children with medulloblastoma: a pilot study. AJNR Am J Neuroradiol. 2003; 24(4): 734–740, indexed in Pubmed: 12695214.
  72. Mabbott DJ, Noseworthy MD, Bouffet E, et al. Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: correlation with IQ. Neuro Oncol. 2006; 8(3): 244–252, doi: 10.1215/15228517-2006-002, indexed in Pubmed: 16723629.
  73. Khong PL, Leung LHT, Fung ASM, et al. White matter anisotropy in post-treatment childhood cancer survivors: preliminary evidence of association with neurocognitive function. J Clin Oncol. 2006; 24(6): 884–890, doi: 10.1200/JCO.2005.02.4505, indexed in Pubmed: 16484697.
  74. Makola M, Douglas Ris M, Mahone EM, et al. Long-term effects of radiation therapy on white matter of the corpus callosum: a diffusion tensor imaging study in children. Pediatr Radiol. 2017; 47(13): 1809–1816, doi: 10.1007/s00247-017-3955-1, indexed in Pubmed: 28844078.
  75. Prust MJ, Jafari-Khouzani K, Kalpathy-Cramer J, et al. Standard chemoradiation for glioblastoma results in progressive brain volume loss. Neurology. 2015; 85(8): 683–691, doi: 10.1212/WNL.0000000000001861, indexed in Pubmed: 26208964.
  76. Nazem-Zadeh MR, Chapman CH, Chenevert T, et al. Response-driven imaging biomarkers for predicting radiation necrosis of the brain. Phys Med Biol. 2014; 59(10): 2535–2547, doi: 10.1088/0031-9155/59/10/2535, indexed in Pubmed: 24778364.
  77. Chapman CH, Zhu T, Nazem-Zadeh M, et al. Diffusion tensor imaging predicts cognitive function change following partial brain radiotherapy for low-grade and benign tumors. Radiother Oncol. 2016; 120(2): 234–240, doi: 10.1016/j.radonc.2016.06.021, indexed in Pubmed: 27418525.
  78. Bian Y, Meng Li, Peng J, et al. Effect of radiochemotherapy on the cognitive function and diffusion tensor and perfusion weighted imaging for high-grade gliomas: A prospective study. Sci Rep. 2019; 9(1): 5967, doi: 10.1038/s41598-019-42321-8, indexed in Pubmed: 30979930.
  79. Law N, Bouffet E, Laughlin S, et al. Cerebello-thalamo-cerebral connections in pediatric brain tumor patients: impact on working memory. Neuroimage. 2011; 56(4): 2238–2248, doi: 10.1016/j.neuroimage.2011.03.065, indexed in Pubmed: 21473922.
  80. Liu K, Ma Z, Feng L. Apparent diffusion coefficient as an effective index for the therapeutic efficiency of brain chemoradiotherapy for brain metastases from lung cancer. BMC Med Imaging. 2018; 18(1): 30, doi: 10.1186/s12880-018-0275-3, indexed in Pubmed: 30223786.
  81. Feng A, Yuan P, Huang T, et al. Distinguishing Tumor Recurrence From Radiation Necrosis in Treated Glioblastoma Using Multiparametric MRI. Acad Radiol. 2022; 29(9): 1320–1331, doi: 10.1016/j.acra.2021.11.008, indexed in Pubmed: 34896001.



Reports of Practical Oncology and Radiotherapy