Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients (2025)

Abstract

Background

Radiotherapy (RT) and chemotherapy are components of standard multi-modality treatment of high grade gliomas (HGG) aimed at achieving local tumor control. Treatment is neurotoxic and RT plays an important role in this, inducing damage even distant to the RT target volume.

Purpose

This retrospective longitudinal study evaluated the effect of treatment on white matter and gray matter volume in the tumor-free hemisphere of HGG patients using voxel based morphometry (VBM).

Method

3D T1-weighted MR images of 12 HGG patients at multiple timepoints during standard treatment were analyzed using VBM. Segmentation of white matter and gray matter of the tumor-free hemisphere was performed. Multiple general linear models were used to asses white matter and gray matter volumetric differences between time points. A mean RT dose map was created and compared to the VBM results.

Results

Diffuse loss of white matter volume, mainly throughout the frontal and parietal lobe, was found, grossly overlapping regions that received the highest RT dose. Significant loss of white matter was first noticed after three cycles of chemotherapy and persisted after the completion of standard treatment. No significant loss of white matter volume was observed between pre-RT and the first post-RT follow-up timepoint, indicating a delayed effect.

Conclusion

This study demonstrated diffuse and early-delayed decreases in white matter volume of the tumor-free hemisphere in HGG patients after standard treatment. White matter volume changes occurred mainly throughout the frontal and parietal lobe and grossly overlapped with areas that received the highest RT dose.

Introduction

High-grade gliomas (HGG) are the most common form of malignant primary brain tumors, associated with a poor prognosis despite treatment [1, 2]. Radiotherapy (RT) and adjuvant chemotherapy are main elements in the treatment of HGG [3, 4]. After surgical resection of tumor, RT and chemotherapy aim at residual microscopic infiltrating tumor cells and are therefore both essential in achieving local control. A total radiation dose of 60 Gy, administered as 30 fractions of 2 Gy, is delivered at a target volume, consisting of the resection cavity or residual tumor plus a 1–2 cm margin. However, despite optimal planning, irradiation of macroscopic healthy brain tissue is inevitable, inducing RT-induced brain damage.

Previous MRI studies have shown that fractionated RT is able to cause structural changes of the white matter and gray matter of the brain, even outside and distant to the RT target volume [5–8]. These changes have been associated with neurocognitive decline which develops in nearly all HGG patients treated with RT [9, 10]. It is known that RT-induced damage to the hippocampus is associated with cognitive decline and is therefore generally spared [11]. Morphologic changes to other specific brain areas due to RT exposure are yet to be better understood.

Voxel Based Morphometry (VBM) analysis allows for voxel-wise comparison of tissue volume changes between different MRI timepoints for a population [12, 13]. VBM analysis has previously shown to be very useful in detecting longitudinal volume changes in glioma patients [14–16]. However if, and to what extent treatment affects healthy contralateral brain tissue during treatment, remains largely unknown. In this study, we evaluated the effect of standard treatment with RT and chemotherapy on gray and white matter volume of the tumor-free hemisphere of patients with unilateral HGG using VBM, aiming at the identification of the most vulnerable neuroanatomical brain areas.

Methods

Study population

We retrospectively included patients 18 to 80 years of age with histologically confirmed HGG, who underwent concomitant chemoradiotherapy (CCRT) with temozolomide (TMZ) followed by adjuvant chemotherapy according to the Stupp protocol [3] at the University Medical Center of Groningen (UMCG) between 2017 and 2020. Other inclusion criteria were acquisition of imaging follow-up after RT with at least a 3D T1-weighted MRI. Dexamethasone was allowed to be given as needed to control symptoms caused by cerebral edema. This resulted in the initial selection of 23 patients. The exclusion criteria were the following: the presence of HGG lesions in both hemispheres (N = 4), the presence of preexisting brain lesions or other brain abnormalities (N = 3), a history of prior brain surgery or irradiation (N = 0), different RT dosage (N = 1), missing MRI data (N = 0) or MRI data not conform with the study specific sequence or protocol (N = 3). The study was approved by the institutional review board and the need for written informed consent was waived.

MRI data acquisition

All patients were scanned on a 1.5T Siemens Magnetom Aera scanner (Siemens Healthcare, Erlangen, Germany) with a 20-channel head coil between 2017 and 2020 in the UMCG. Multiple 3D T1-weighted sagittal scans at different timepoints were acquired of all patients: i) pre-RT, ii) post-CCRT, iii) after three cycles of TMZ and iv) after six cycles of TMZ. 3D T1 MP-RAGE images were acquired (repetition time [TR] 2200 ms, echo time [TE] 2.67 ms, inversion time [TI] 900 ms, field of view [FOV] 230 x 230 mm², matrix 256 x 256, slice thickness 1 mm, no spacing, resolution 0.96 mm³, voxel size 1 x 0.977 x 0.977 mm³, flip angle 8°).

RT planning

RT planning was performed on the reference CT image of each patient using Mirada RTx (Mirada medical, Oxford, UK). The RT planning technique used in all plans was volumetric modulated arc therapy (VMAT) with or without a static conformal non-coplanar beam. All patients received a total radiation dose of 60 Gy, administered as 30 fractions of 2 Gy daily during 6 weeks. The clinical target volume (CTV) consisted of the resection cavity and/or residual tumor plus a 1.5 cm margin without dose spillage to the contralateral hemisphere. Using the AI autocontouring function of Mirada RTx, the cerebrum was contoured on all CT images and then manually divided to acquire RT dose distribution data of the total cerebrum and of each hemisphere.

Furthermore, a mean RT dose map was created using 3D Slicer version 4.8.1 (http://www.slicer.org). Firstly, the maps of the RT dose distribution and corresponding reference CT images of each patient were acquired and selectively flipped to ensure the tumor-containing hemisphere was identical for all data. Secondly, all RT dose maps and CT images were coregistered using the general registration (Elastix) function in 3D Slicer after which a mean RT dose map for the cohort was created with the Radiotherapy Dose Accumulation function. Finally, the mean RT dose map was coregistered to the T1 weighted Montreal Neurological Institute (MNI) brain. In doing so, coordinates were transformed into a common coordinate space which allowed for identification of brain regions that received the highest RT dose.

Image analysis

Image processing and voxel-based statistical analysis were conducted using Statistical Parametric Mapping (SPM), version 12 (Wellcome Department of Imaging Neuroscience Group, London, UK). Firstly, the T1-weighted images were reorientated with affine transformations to align these images with the anterior commissure–posterior commissure (AC-PC) line. Secondly, the images were selectively flipped to ensure the “tumor-containing” hemisphere was identical for all images. This was followed by applying the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) tool to create a study specific template [17]. Segmentation into gray matter, white matter and cerebrospinal fluid was performed following standard procedures [18, 19]. Gray and white matter templates were used to normalize the gray and white matter images of each subject to the respective template; the resulting normalized images were modulated with a Jacobian correction; and an 8mm full width at half maximum Gaussian kernel was used to smooth all normalized and modulated images. The tumor-containing hemisphere was masked during all analyses as SPM is unable to correctly identify and segment gray matter and white matter in the proximity of tumors. Together with the selective flipping, this process ensures that the “tumor-free” hemisphere can be reliably analyzed [20].

Statistical analysis of MRI data

Multiple general linear models were setup in SPM to asses gray and white matter volumetric differences between different time points. Post hoc, paired t tests were applied to compare gray and white volume between two distinct time points. To account for differences in brain size, the intracranial volume of each scan was calculated and used as a proportional correction factor (i.e. global normalization). VBM analysis was carried out with the voxel threshold set at uncorrected p < 0.001 (p_unc) and cluster threshold set according to p < 0.05 after family-wise error correction (p_FWEc).

Results

Patient characteristics

A total of 12 patients were included in this study with a median age at RT of 54.4 years (16.4 interquartile range [IQR], 45.6–62.0). See Table 1 for patient characteristics. Based on the diagnostic algorithm for the classification of gliomas in adults by the European council of neuro-oncology, a single grade II glioma patient was considered as having an HGG due to molecular markers [21]. The O(6)-methylguanine-DNA methyltransferase (MGMT) promotor status was known in 9/12 (75%) patients. MGMT was methylated in 6 patients (50%) and unmethylated in 3 patients (25%). The mean RT dose of the total cerebrum, tumor-containing hemisphere and tumor-free hemisphere were 25.2 (4.15 IQR, 24.4–28.6) Gy, 33.5 (6.38 IQR, 31.6–38.0) Gy and 17.1 (6.93 IQR, 13.5–20.4) Gy, respectively. The highest measured mean RT dose was 35.2 Gy. An average dose distribution map is shown in Fig 1 and demonstrates the highest mean RT dose (20–30 Gy) was delivered at the parietal and occipital lobes and thalamus, and a lower dose (10–20 Gy) was received in the frontal and temporal lobes and basal ganglia.

Table 1. Demographic and clinical parameters.

Parameter	All patients n = 12 (%)
*Gender*
Male	6 (50.0)
Female	6 (50.0)
*Mean age at RT (years) [IQR]*	54.4 [16.4, 45.6–62.0]
*Mean intercranial volume (L) [IQR]*	1.50 [0.166, 1.40–1.56]
*Tumor-containing hemisphere (R/L) / lobe*
Right	5 (41.7)
Frontal	2
Temporal	1
Frontal/temporal	1
Parietal/occipital	1
Left	7 (58.3)
Hippocampal	1
Occipital	1
Parietal	1
Temporal	1
Frontal/parietal	1
Frontal/temporal	1
Parietal/occipital	1
*Initial surgical procedure*
Resection	10 (83.3)
Biopsy	2 (16.7)
*Tumor grade*
Grade IV	9 (75.0)
Grade III	2 (16.7)
Grade II, GBM markers +*	1 (8.33)
*MGMT promotor status*
Methylated	9 (75.0)
Unmethylated	2 (16.7)
Missing	1 (8.33)

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Table with demographic and clinical information of study population.

*Based on the diagnostic algorithm for the classification of gliomas in adults by the European council of neuro-oncology, this glioma was classified as a high grade glioma. Abbreviations: GBM = glioblastoma; MGMT, O(6)-methylguanine-DNA methyltransferase; RT = Radiotherapy.

Fig 1. Mean radiotherapy dose map of the study population.

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The preRT scan was on average 67.4 days (7.25 IQR, 65.3–72.5) before RT. The first post-RT scan was on average 21.8 days (4.75 IQR, 20.8–25.5) after RT. The second post-RT scan was on average 113 days (6.00 IQR, 105–111) after RT. The third post-RT scan was on average 201 days (11.5 IQR, 189–201) after RT. Two patients had no eligible MRI after six cycles of TMZ due to tumor progression causing the tumor to infiltrate over the midline. A total of 46 MR images were used in the analysis.

White matter analysis

Voxel-wise comparison of white matter volume revealed significant clusters indicating white matter volume loss after radiotherapy (Table 2). Comparison of pre-RT images with post-RT images revealed no significant clusters for volumetric difference in white matter (Table 2). Two significant clusters were found for white matter volume loss, extending throughout the corpus callosum, cingulate gyri (anterior, middle and posterior gyrus) and the frontal (precentral and superior, middle and inferior gyrus), parietal (postcentral, supramarginal, angular and inferior parietal gyri), temporal (superior temporal gyrus) and occipital (lingual, calcarine, precuneus, cuneus, fusiform, superior and middle occipital gyrus) lobe, when comparing pre-RT images with the images after three cycles of TMZ (Fig 2A). Three significant clusters for white matter volume loss were found in similar areas with the addition of the insula, putamen, pallidum, superior parietal and inferior occipital gyrus when comparing pre-RT images and the images after 6 cycles of TMZ (Fig 2B).

Table 2. White matter volume differences per timepoint comparison.

Brain region	Cluster-level		Peak-level		MNI coordinates
	p_FWEc	K_E	T	Z_E	x	y	z
PreRT versus post-RT (4.02
No significant clusters	-	-			-	-	-
PreRT versus after 3 cycles of Temozolomide (4.02)
Calcarine sulcus	<0.001	13062	5.84	3.86	24	-59	11
PreRT versus after 6 cycles of Temozolomide (4.30)
Supramarginal gyrus	<0.001	27247	10.97	4.79	50	-39	26
Corpus callosum	<0.001	2157	7.01	4.00	8	-27	17
Post-RT versus after 3 cycles of Temozolomide (4.02)
Midcingulate cortex	<0.001	5678	11.18	5.17	8	-38	33
Putamen	<0.001	1176	6.95	4.22	32	-8	5
Hippocampus	0.007	653	6.63	4.13	38	-15	-11
Supramarginal gyrus	0.005	692	6.12	3.96	59	-20	30
Superior temporal gyrus	0.004	708	5.61	3.78	48	17	2
Post-RT versus after 6 cycles of Temozolomide (4.30)
Postcentral gyrus	<0.001	1568	7.10	4.03	27	-38	57
Thalamus	0.003	551	7.71	4.30	12	-12	9
Corpus callosum	0.014	770	8.28	4.18	6	-26	17
After 3 cycles of Temozolomide versus after 6 cycles of Temozolomide (4.30)
No significant clusters	-	-	-	-	-	-	-

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Overview of the significant clusters for white matter volume loss of time-group comparisons. Brain regions are reported for the peak levels of each cluster according to the automated anatomical labeling template. Abbreviations: pFWEc = p-value after familywise error correction, KE = cluster size, MNI = Montreal Neurological Institute, RT = Radiotherapy and ZE = Z-value of cluster.

Fig 2. Significant clusters for white matter volume loss per timepoint comparison.

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Additionally, when comparing post-RT images with images after three cycles of TMZ, five significant clusters for white matter volume loss were found throughout the corpus callosum, putamen, pallidum, hippocampus, parahippocampal zone, insula, cingulate gyri (anterior, middle and posterior gyrus) and frontal (inferior gyrus), parietal (postcentral, supramarginal and inferior parietal gyrus), temporal (superior temporal) and occipital (calcarine, precuneus, cuneus and superior occipital gyrus) lobe (Fig 2C). Three small clusters for white matter volume loss were found, mostly located in the corpus callosum, putamen, pallidum, insula, cingulate gyri (middle and posterior gyrus) and parietal (supramarginal and postcentral gyrus) and temporal (superior and middle temporal gyrus) lobe when comparing post-RT images with the images after six cycles of TMZ (Fig 2D). No significant clusters were found for volumetric difference of white matter when comparing the images after three cycles and after six cycles of TMZ.

Gray matter analysis

Voxel-wise comparison of gray matter volume revealed two significant clusters indicating gray matter volume loss when comparing images after three cycles and six cycles of TMZ (Table 3, Fig 3). One cluster was located within the putamen and pallidum and the other was located in the cerebellum. No other significant clusters for volumetric difference of gray matter were found for any of the other time-group comparisons (Table 3).

Table 3. Gray matter volume differences per timepoint comparison.

Brain region	Cluster-level		Peak-level		MNI coordinates
	p_FWEc	K_E	T	Z_E	x	y	z
*PreRT versus post-RT*
No significant clusters	-	-	-	-	-	-	-
*PreRT versus after 3 cycles of Temozolomide*
No significant clusters	-	-	-	-	-	-	-
*PreRT versus after 6 cycles of Temozolomide*
No significant clusters	-	-	-	-	-	-	-
*Post-RT versus after 3 cycles of Temozolomide*
No significant clusters	-	-	-	-	-	-	-
*Post-RT versus after 6 cycles of Temozolomide*
No significant clusters	-	-	-	-	-	-	-
*After 3 cycles of Temozolomide versus after 6 cycles of Temozolomide*
Globus pallidus	0.036	459	9.74	4.59	27	-9	2
Cerebellar Crus I	0.025	506	8.85	4.42	23	-84	-29

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Overview of the significant clusters for gray matter volume loss of time-group comparisons. Brain regions are reported for the peak levels of each cluster according to the automated anatomical labeling template. Abbreviations: p_FWEc = p-value after familywise error correction, K_E = cluster size, MNI = Montreal Neurological Institute, RT = Radiotherapy and Z_E = Z-value of cluster.

Fig 3. Significant clusters for gray matter volume loss per timepoint comparison.

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Discussion

In this retrospective longitudinal study, VBM analysis demonstrated treatment-induced volumetric changes over time in gray and white matter of the tumor-free hemisphere of 12 HGG patients. We detected diffuse loss of white matter volume, mainly throughout the frontal and parietal lobe of the tumor-free hemisphere. Significant loss of white matter was first noticed after three cycles of TMZ, approximately 16 weeks post-RT, mainly throughout the frontal and parietal lobe. Similar areas of white matter volume loss were observed after the completion of the Stupp protocol. However, no significant loss of white matter volume was observed between pre-RT and the first post-RT follow-up timepoint, indicating a delayed effect.

Early-delayed RT damage, occurring weeks to months after RT, leading to white matter volume loss is a multifactorial process and most likely due to transient demyelination, blood-brain barrier disruption and neuroinflammation [22–24]. This RT-induced decrease in white matter volume has also been previously demonstrated [25, 26]. Our study, however, offers a unique group-based analysis with identification of common white matter areas most affected by multi-modality treatment. Previous studies using diffusion tensor imaging (DTI) have found similar results. However, these studies only provide a limited insight in the localization of white matter volume loss within the brain. One study by Conner et al found that normal appearing white matter receiving >30 Gy showed significant diffusion changes four to six months after RT [6]. In line with our results, no significant diffusion changes were observed one month after RT. Another DTI study analyzed multiple distinct regions of interest within the normal appearing white matter of glioblastoma patients receiving 60 Gy fractionated cranial RT [7]. Similar to our findings, it was established that the white matter of the corpus collosum, anterior cingulate and fornix were most susceptible to injury as a result of high dose fractionated cranial RT. A study combining VBM analysis and DTI among 14 glioblastoma patients, however, found no significant volume changes of white matter in the tumor-free hemisphere during treatment [27]. However, this study evaluated white matter as a whole, rather than the cluster-based analysis which our study employed. It should also be noted that VBM measured volume changes cannot directly be compared to DTI-based changes, with the latter technique demonstrating different macro/micro changes than VBM analysis.

No significant loss of white matter volume was noticed between the second (after three cycles of TMZ) and third (after completion of the Stupp protocol) post-RT timepoints, suggesting that RT-induced volume loss was not a progressive process in our cohort. Furthermore, these brain areas showing a delayed loss of white matter volume from baseline were comparable for both the pre-RT and the first post-RT baseline timepoints. However, when compared to the pre-RT baseline, the found clusters were more compact in size than compared to the first post-RT timepoint. Although we did not find any significant clusters for white matter volume increase between any timepoint comparison, these findings may indicate that white matter partially recovers. Partial recovery of RT-induced white matter damage has also been described by an earlier DTI study [28]. In this study among eight patients treated with RT, transient white matter changes of the tumor-free hemisphere were demonstrated after three to five months post-RT which recovered at later timepoints of six to nine months and 10–12 months after completion of RT.

Diffuse loss of gray matter and cortical thinning after high-dose RT has been previously described [29–31]. A study among 15 HGG patients found a dose-dependent thinning of the cortical surface one year after RT [32]. Similar findings were described in studies by Seibert et al and Nagtegaal et al [33, 34]. We did not observe any significant gray matter volume difference at any of the post-RT follow-up timepoints compared to the pre-RT baseline. However, two small areas of gray matter volume loss in the globus pallidus and cerebellar crus, were found when comparing images of the third and fourth post-RT follow-up timepoint. Volume decline of deep subcortical gray matter structures, including the globus pallidus, have also been previously demonstrated by another study among 31 glioma patients [31]. It might be possible that gray matter changes due to high-dose RT are a delayed process. However, the end point of this study was after completion of the Stupp protocol (6 months post-RT), which does not allow measurement of delayed effects. Moreover, the aforementioned studies measured cortical thinning across the entire brain whereas we studied the tumor-free hemisphere only, possibly explaining the differences in results.

Comparing our VBM results with our mean RT dose map indicates that regions of white matter volume loss within the thalamus, caudate nucleus, middle and posterior cingulate cortex, corpus callosum and the parietal, temporal and occipital lobe grossly overlap with areas exposed to the highest RT dose (20–30 Gy). These results suggest that, although RT and chemotherapy are both neurotoxic, RT has a strong relationship with the observed white matter volume loss. However, more frontally located areas such as the frontal lobe, anterior cingulate cortex and insula received a lower RT dose but also experienced white matter volume loss. Similar findings of widespread morphological changes in the entire brain were also observed in a VBM study by Nagtegaal et al [35], which utilized pre-RT and post-RT MRI scans of 28 glioma patients. Furthermore, they established that volume changes were dose-dependent. Within our study, despite heterogenicity of the RT field across the study population, group results still yielded common areas affected by RT, indicating a strong relationship between any RT dose and the vulnerability of these areas to RT. This potentially indicates enhanced radiosensitivity of aforementioned more frontally located brain areas.

The largest limitation of this study was the limited sample size, which was a direct consequence of the high requirements of the inclusion and exclusion criteria. To improve the reliability of our results, the analysis was performed on 3D T1-weighted MRI sequences for adequate volume calculations and over multiple timepoints. Additional larger VBM studies are therefore necessary to validate our findings. The small sample size also hindered a voxel threshold with familywise error correction set at <0.05. Future studies should aim to include a larger patient population so that familywise error correction can be utilized at voxel level. Secondly, abnormalities such as tumors within the brain are not reliably segmented into the correct tissue class, making analysis in areas in close proximity to the tumor unreliable. Hence, our analysis was restricted to the tumor-free hemisphere of the brain. Thirdly, the distribution of radiation exposure was not identical across the study population due to difference in tumor location. The unavailability of RT dose maps hindered a direct voxel-wise comparison between RT dose and white or gray matter volume changes on the individual level. Future studies should aim to incorporate RT dose maps into the analysis. However, despite individual differences in RT dose maps, our group-based results still showed common areas affected by standard treatment, probably due to the vulnerability of these areas and their strong relation with any RT dose. Furthermore, the MGMT promotor status was not known for all patients, and its impact of our results was not tested. MGMT methylated tumors are more sensitive to alkalizing chemotherapy and might therefore respond differently than unmethylated tumors in terms of volume change. Finally, unfortunately no neurocognitive evaluation of the HGG patients was available. Future prospective studies should include neurocognitive testing to relate imaging findings to the clinical setting and the impact of patient neurocognitive functioning.

Conclusion

This study demonstrated diffuse decreases in white matter volume of the tumor-free hemisphere in HGG patients after standard multi-modality treatment according to the Stupp-protocol. These volume changes occurred in the early-delayed phase and were not progressive in nature, suggesting partial recovery. These findings provide an insight into the mechanism of treatment-induced damage to the tumor-free brain tissue in treated HGG patients and suggest that caution should be taken with RT target volume dose planning to minimize white matter injury.

Abbreviations

CCRT: Concomitant chemoradiotherapy
DARTEL: Diffeomorphic anatomical registration through exponentiated lie algebra
DTI: Diffusion tensor imaging
HGG: High grade glioma
RT: Radiotherapy
SPM: Statistical parametric mapping
TMZ: Temozolomide
VBM: Voxel based morphometry

Data Availability

Data will not be made available to other researchers as no patient approval has been obtained for sharing coded data. Syntax files will be made available from the Loket Contract Research, section of the UMCG’s Department of Legal Affairs, on reasonable request (email address: loket_contract_research@umcg.nl).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0275077.r001

Kevin Camphausen

4 Jan 2023

PONE-D-22-25162Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patientsPLOS ONE

Dear Dr. Van Dijken,

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Reviewer #2:Partly

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Reviewer #1:No

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Reviewer #1:The manuscript aims to tackle an important question, that of white matter volume loss after multi-modality

treatment in high grade glioma patients. They aim to do this by analyzing the impact of radiation therapy dose distribution on areas of the brain that are not believed to be directly involved with tumor.

pre-RT and the first post-RT follow-up timepoint concluding a possible delayed effect. This has been described and the authors note this in the discussion.

These findings are interesting but are likely far more nuanced.

The most significant aspects that if added would enrich the value of the findings, are the addition of information with respect to patient, disease and radiation therapy characteristics. The authors acknowledge that both RT and chemo are neurotoxic. The nuance here is that if patients are MGMT methylated (status not reported) and/or have more significant tumor burden ( GTV, CTV not reported) or had a larger PTV ( not reported) or more significant T2 FLAIR abnormality (was this treated? to 60Gy, to 46 Gy?), were treatments single phase one vol to 60 Gy or two phase with differential margin for T1 gad vs T2 FLAIR ( not reported), all of these aspects would affect dose and volume and dose spillage to the contralat hemisphere. Did the radiation oncologist(s) allow for PTV spillage into the contralateral hemisphere? ( some do and some do not for HGG).

If MGMT methylated, response to chemo in chemo sensitive cells will likely also have a role in white matter changes, particularly if larger tumor or larger margins/PTV especially considering the infiltrative nature of the disease. There is no analysis of the RT dose volume histogram which the authors mention as a limitation but this is a crucial aspect to define the dose to volume relationships to be able to generate conclusions. All the patients were treated VMAT but this does not tell the whole story of the dose distribution and is too broad of a common denominator to assume that given more detailed treatment planning information the conclusion would remain the same.

It is also not clear how CT images were manually divided to acquire RT dose distribution data of the total cerebrum and of each hemisphere. Why not use the RT treatment planning system functionality for this? and report mean and max doses in relationship to the organs at risk, in this case the vulnerable areas of the brain, that the paper wishes to explore dose/white matter change relationships to.

in its current format, the methodology is interesting but the findings are not detailed enough to be more that descriptive although with additional information, there is great potential.

Reviewer #2:The manuscript by Jesse D. de Groot et al, titled as “Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients” try to evaluated the effect of standard treatment with RT and chemotherapy on gray and white matter volume of the tumor-free hemisphere of patients with unilateral high-grade gliomas using longitudinal voxel based morphometry analysis with SPM.

In the introduction section, I’d suggest to add additional references with longitudinal VBM-based analysis, and not to limit with Parkinson disease. Moreover, it would be more adequate adding references for longitudinal brain changes, including functional, structural and morphometric changes, in patients with brain tumours. There are already several studies reporting longitudinal changes (such as VBM, DTI, perfusion MRI), including pre- and post-treatment in patients with brain tumours (Hye In Lee et al 2022; Hu et al, 2020; Cayuela N et al 2019; Fathallah-Shaykh et al 2019).

From the prospective of similar studies, it should be better introduce what has been done by previous research and the notch that will be added by current study.

In the Material and Method section, RT planning should be moved after MRI acquisition protocol. Missing temporal windows of serial MRI acquisitions.

Authors mentioned in the statistical analysis that paired t tests were applied to compare grey and white volume between two distinct time points. I guess, voxel-wise repeated measure ANOVA was not run. Please, explain the selection of the statistical approach.

Please, explain also why the voxel level PFWE < 0.05 has not been chosen for the level of significance. This fact should be properly addressed and should be also added in the limitation section.

In the Table 2, the voxel-level information should be added with T-statistics

Missing colour-bar for the figure 2 and 3

How the authors address the RT-related changes vs plasticity-related changes in the brain. Does serial neurocognitive assessment available for the patients?

In the discussion section, it should be mentioned that white matter VBM compared to DTI-based analysis are not the same, and shows different macro/micro changes.

It might be interesting to address the interplay between received RT-dose and white and grey matter changes either voxel-wise or by extracting mean volume for each subject from the significant clusters.

Some typos – “gray matter” instead of “grey matter”

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Reviewer #1:No

Reviewer #2:No

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PLoS One. 2023 May 3;18(5):e0275077. doi: 10.1371/journal.pone.0275077.r002

14 Feb 2023

Author response to reviewers' comments

Reviewer #1

Comment 1:

The manuscript aims to tackle an important question, that of white matter volume loss after multi-modality treatment in high grade glioma patients. They aim to do this by analyzing the impact of radiation therapy dose distribution on areas of the brain that are not believed to be directly involved with tumor.

The authors find diffuse loss of white matter volume, mainly throughout the frontal and parietal lobe of the tumor-free hemisphere and significant loss of white matter after three cycles of TMZ, approximately 16 weeks post-RT, throughout the frontal and parietal lobe but no significant loss of white matter volume between pre-RT and the first post-RT follow-up timepoint concluding a possible delayed effect. This has been described and the authors note this in the discussion. These findings are interesting but are likely far more nuanced.

Answer:

We would like to thank the reviewer for critically reading our manuscript and providing the suggestions made below. We agree with the reviewer that treatment-induced brain damage in glioma patients is an important topic. In this study, we aimed to provide an insight into one of the possible mechanisms of treatment-induced damage to the tumor-free brain tissue by evaluating white matter volume changes over time in treated high-grade glioma patients.

Comment 2:

Answer:

The reviewer correctly discussed several significant clinical and radiotherapeutic parameters that were not mentioned in our manuscript. Unfortunately, not all radiotherapy data were available for our analysis, which we indeed mention as a limitation in the discussion section of our manuscript as noted by the reviewer. However, in line with the reviewer’s suggestions, we have added the following additional information to our manuscript:

All patients received a total radiation dose of 60 Gy, administered as 30 fractions of 2 Gy daily during 6 weeks. The clinical target volume (CTV) consisted of the resection cavity and/or residual tumor plus a 1.5 cm margin without dose spillage to the contralateral hemisphere (page 6).

As suggested by the reviewer, we have checked the MGMT status of our patients and found the following results: 3 patients with missing MGMT status, 3 patients were MGMT unmethylated and 6 patients were MGMT methylated. We included these numbers to table 1 and added the following sentences to the results section: The O(6)-methylguanine-DNA methyltransferase (MGMT) promotor status was known in 9/12 (75%) patients. MGMT was methylated in 6 patients (50%) and unmethylated in 3 patients (25%) (page 8).

We also mentioned the MGMT status as possible limitation in the discussion section: Furthermore, the MGMT promotor status was not known for all patients, and its impact of our results was not tested. MGMT methylated tumors are more sensitive to alkalizing chemotherapy and might therefore respond differently than unmethylated tumors in terms of volume change (page 16).

Comment 3:

Answer:

The radiotherapy planning software which was utilized was only able to automatically outline of the whole cerebrum and cerebellum. Therefore, the ipsilateral hemisphere of the brain was manually captured within a box and was subtracted from the total brain volume to retrieve information on the contralateral hemisphere.

Comment 4:

In its current format, the methodology is interesting but the findings are not detailed enough to be more that descriptive although with additional information, there is great potential.

Answer:

Once again, we would like to thank the reviewer for his/her compliments to our work and the suggestions made. We have added more detailed information to our method section in line with the reviewer’s comments and we feel that these suggestions have indeed strengthened our manuscript.

Reviewer #2

Comment 1:

The manuscript by Jesse D. de Groot et al, titled as “Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients” try to evaluated the effect of standard treatment with RT and chemotherapy on gray and white matter volume of the tumor-free hemisphere of patients with unilateral high-grade gliomas using longitudinal voxel based morphometry analysis with SPM.

Answer:

We thank the reviewer for the constructive feedback presented below, after critically appraising our work. We have addressed the reviewer’s suggestions and added additional information and clarification to our manuscript.

Comment 2:

Answer:

We agree with the reviewer that referring to Parkinson’s disease was distracting and not in line with the objective of our study. We thank the reviewer for providing us with additional references to support our introduction, we have added 4 new references (#10, #14, #15, and #16) to our introduction. We have also replaced the Parkinson reference with the following sentences to be more in line with our study objective and highlight what this study adds to the field:

VBM analysis has previously shown to be very useful in detecting longitudinal volume changes in glioma patients. However if, and to what extent treatment affects healthy contralateral brain tissue during treatment, remains largely unknown (page 4).

Comment 3:

In the Material and Method section, RT planning should be moved after MRI acquisition protocol.

Answer:

We have moved the RT planning paragraph below the MRI acquisition protocol as suggested.

Comment 4:

Answer:

The review correctly concludes that we did not run voxel-wise repeated measure ANOVA, but used pared t tests instead. Repeated measure ANOVA gives no insight in how brain tissue volume changes occur over time. It only indicates if there is a difference between all timepoints. As the goal of our research was to evaluate what the effect of multimodality treatment is over time, repeated measure ANOVA does not provide the requisite information.

Comment 5:

Please, explain also why the voxel level PFWE < 0.05 has not been chosen for the level of significance. This fact should be properly addressed and should be also added in the limitation section.

Answer:

Due to our low sample size of only 12 patients (which was a result of our strict inclusion and exclusion criteria to provide uniformity), a voxel threshold with familywise error correction set at < 0.05 would be too aggressive. Therefore, this is one of the important limitations of our study. In future studies, similar VBM-studies should aim to include a larger patient population so that familywise error correction can be utilized at voxel level. We have added the following sentences to the limitation section to further emphasize this limitation: The small sample size also hindered a voxel threshold with familywise error correction set at <0.05. Future studies should aim to include a larger patient population so that familywise error correction can be utilized at voxel level (page 16).

Comment 6:

In the Table 2, the voxel-level information should be added with T-statistics.

Answer:

We have added this to table 2 as suggested.

Comment 7:

Missing colour-bar for the figure 2 and 3

Answer:

We thank the reviewer for pointing this out and have indeed included color bars to figures 2 and 3.

Comment 8:

How the authors address the RT-related changes vs plasticity-related changes in the brain.

Answer:

In this study it was not possible to directly differentiate treatment-induced volume changes from potential plasticity changes. However, our analysis was not only performed for white and gray matter volume loss, but also for potential increases in volumes. There were no significant clusters for white or gray matter increase between any timepoints. Furthermore, our results demonstrated early-delayed white matter volume decreases after treatment, which grossly overlapped with areas that received the highest RT dose, further suggesting treatment-induced effects.

Comment 9:

Does serial neurocognitive assessment available for the patients?

Answer:

Unfortunately no neurocognitive testing was available for our patient cohort. We agree with the reviewer that the correlation between volume changes and neurocognitive decline, which develops in a large percentage of treated glioma patients, would be very interesting to study further. Therefore, we changed the last two sentences of our limitations paragraph, where we also mention this limitation, to the following:

Finally, unfortunately no neurocognitive evaluation of the HGG patients was available. Future prospective studies should include neurocognitive testing to relate imaging findings to the clinical setting and the impact of patient neurocognitive functioning (page 16).

Comment 10:

In the discussion section, it should be mentioned that white matter VBM compared to DTI-based analysis are not the same, and shows different macro/micro changes.

Answer:

We thank the reviewer for this point as it is true that VBM measured white matter changes cannot directly be translated to DTI findings and vice versa. In line with this comment we have added the following to the discussion: It should also be noted that VBM measured volume changes cannot directly be compared to DTI-based changes, with the latter technique demonstrating different macro/micro changes than VBM analysis (page 14).

Comment 11:

Answer:

Comparing our VBM results with our mean RT dose map for the entire patient cohort, the regions of white matter volume loss grossly overlap with areas exposed to the highest RT dose as we point out in the results and discussion. These results suggest that RT has a strong relationship with the observed white matter volume loss. We agree with the reviewer that it would be interesting to test this hypothesis on the individual patient level, by investigating a relation between received RT-dose and voxel-wise analysis of volume changes. Unfortunately, this was not possible with our data. We have added the following sentence in line with this suggestion: The unavailability of RT dose maps hindered a direct voxel-wise comparison between RT dose and white or gray matter volume changes on the individual level (page 16).

Comment 12:

Some typos – “gray matter” instead of “grey matter”

Answer:

Throughout the manuscript we used U.S. English spelling rather than U.K. English, leading to altered spelling of certain frequently mentioned words as “gray” and “tumor”. We have checked the manuscript thoroughly and believe we consequently spelled these words in line with the U.S. English standard.

PLoS One. doi: 10.1371/journal.pone.0275077.r003

Kevin Camphausen

7 Mar 2023

Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients

PONE-D-22-25162R1

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PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0275077.r004

Kevin Camphausen

13 Mar 2023

PONE-D-22-25162R1

Voxel based morphometry-detected white matter volume loss after multi-modality treatment in high grade glioma patients

Dear Dr. Van Dijken:

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