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. 2026 Sep 7;8(1):vdag241. doi: 10.1093/noajnl/vdag241

Poor brain reserve increases mortality risk in glioblastoma

Sree Gongala 1, Robin Ghotra 2, George Wang 3, Jose A Garcia 4,5, Rubo Xing 6, Parisa Arjmand 7,8, Pamela LaMontagne 9,10, Carmen Balana 11, Josep Puig 12, Danial Marcus 13, Yilun Sun 14, Wassim Malak 15, Haley Perlow 16,17, Tiffany R Hodges 18,19, Christos Davatzikos 20,21,22,†, Chaitra Badve 23,24,†,✉
PMCID: PMC13629547  PMID: 42825191

Abstract

Background

Brain reserve refers to the structural capacity of the brain and correlates with age and Karnofsky Performance Status (KPS). While age and KPS are known independent predictors of survival in glioblastoma patients, the role of brain reserve in influencing glioblastoma outcomes remains unexplored. This study investigates the relationship between baseline brain-reserve metrics and survival outcomes in isocitrate dehydrogenase 1-wildtype (IDH1-WT) glioblastoma.

Methods

We analyzed 841 patients with IDH1-WT glioblastoma using clinical data and presurgical MR images sourced from 4 institutions. MRI-based tumor characteristics and quantitative brain-reserve metrics were assessed using the Cancer Imaging Phenomics Toolkit and the Computational Anatomy Toolbox. Statistical and survival analyses were conducted using MATLAB and R.

Results

Long-term survivors showed significantly higher residual functional gray matter volume (rfGM) (P < .001), gyrification (P = .001), and cortical thickness (P < .001) compared to medium- and short-term survivors. Multivariable Cox analysis indicated significant associations between rfGM (P < .001) and gyrification (P = .008) with mortality after adjusting for clinical variables except age and KPS. After controlling for all clinical variables including age and KPS, gyrification (P = .01), sulcal depth (P = .02), and cortical thickness (P = .005) were significantly associated with mortality.

Conclusion

This multi-institutional study establishes significant associations between structural brain-reserve metrics and glioblastoma outcomes, independent of age and KPS. The findings highlight the potential of brain reserve as a new biomarker for brain tumor prognosis and treatment planning.

Keywords: age, brain reserve, glioblastoma, Karnofsky performance status, survival outcomes

Key Points.

  • Long-term survivors show greater residual functional gray matter, gyrification, and cortical thickness.

  • Brain-reserve metrics influence survival independent of age and Karnofsky Performance Status.

  • Brain reserve may have a prognostic role in brain tumor management.

Importance of the Study

Brain reserve is significantly correlated with an individual’s age and performance status. While age and performance are well-established predictors of glioblastoma outcomes, there is a limited understanding about the impact of brain reserve on outcomes. In this study, we demonstrate that baseline brain-reserve metrics are independently associated with survival in glioblastoma patients. These findings highlight their potential as a clinical biomarker for patient prognosis and treatment planning.

Brain reserve refers to quantifiable neurobiological characteristics of the central nervous system, such as total brain parenchymal volume, gray and white matter volumes, neuronal number, and synaptic density, and can be measured with MR imaging and histopathology.1 Cognitive reserve, on the other hand, relates to a person’s ability to maintain cognitive functions and is influenced by their education, experience, and occupation. Both brain and cognitive reserve indicate an individual’s capacity to cope with aging and disease.1 Brain reserve is strongly correlated to an individual’s cognitive reserve, performance status, and age.2,3 Decline in brain reserve has been noted across various neurological disorders and malignancies, including Alzheimer’s disease, multiple sclerosis, breast cancer, and leukemia.4-7 The prognostic role of brain reserve in primary brain tumors, including glioblastoma outcomes, remains unexplored.

Decline in both cognitive and brain reserve has been noted in glioma patients. Cognitive impairments such as impaired memory retention and executive functions are frequently observed in glioblastoma patients.8 This cognitive decline is modulated by tumor-specific factors, including laterality, anatomical location, extent of disease progression, and patients’ baseline neurocognitive status.9-11 Cross-sectional morphometry studies have demonstrated that glioma patients also exhibit lower brain reserve compared to healthy controls, with significant gray matter loss and cortical thinning in the contralateral hemisphere.12,13 This decline appears to worsen following chemoradiation therapy, with a predominant reduction in whole brain and gray matter volumes.14 Despite these studies, in-depth research focusing specifically on brain reserve and its relationship to survival outcomes in glioblastoma patients remains limited. The brain reserve studies to date either include all glioma types or are based on older WHO classifications. None of the studies focus on isocitrate dehydrogenase 1-wildtype (IDH1-WT) glioblastoma, which has a distinctly accelerated clinical course compared to other glioma subtypes.15

Glioblastoma is a highly aggressive primary brain tumor, with a median survival of about 9 months.16,17 Age and Karnofsky Performance Status (KPS) are independently associated with survival, with elderly patients (65 years and older) and those with a low KPS (less than 80) suffering worse outcomes.18 Notably, survival outcomes in elderly patients with preserved functional status, as indicated by higher pre- and postoperative KPS scores, are similar to those of younger individuals when both groups receive aggressive multimodal therapy.18-20 These findings suggest that brain reserve, by influencing performance status, may serve as a potential predictor of patient outcomes.

Brain reserve is quantified using parameters such as whole and fractional brain volume, cortical thickness, sulcal depth, and gyrification.1,21 Assessing brain reserve in a clinical setting is an achievable goal since the majority of brain tumor patients undergo MRI examinations. The purpose of this study was to determine whether brain-reserve metrics could serve as imaging-based prognostic markers in glioblastoma. In this retrospective multicenter study, we characterized baseline MRI-derived structural brain-reserve metrics in patients with IDH1-WT glioblastoma and investigated their associations with overall survival, stratified by age and adjusted for clinical parameters.

Methods

Patient Population

This retrospective study included data obtained from 2 primary sources. (1) University Hospitals Cleveland Medical Center, where clinical, molecular, and imaging data from patients with IDH1-WT glioblastoma treated between 2012 and 2025 were collected under Institutional Review Board approval with a waiver of informed consent. (2) A multi-institutional cohort from the Radiomics Signatures for PrecisiON Diagnostics (ReSPOND) consortium, which aggregates glioblastoma datasets contributed by several international institutions and has been used extensively in prior imaging studies.22-24 After applying the study’s inclusion and exclusion criteria, a total of 841 patients were eligible for analysis, including 117 from the local institution and 724 from 3 ReSPOND consortium sites (Figure 1). Within the consortium subset, the institutional distribution was as follows: University of Pennsylvania (n = 506), Washington University (n = 191), and the Catalan Institute of Oncology (n = 27).

Figure 1.

Flowchart of subject selection starting with 4,638 total subjects (131 local, 4,507 consortium). Sequential exclusions—3,547 low-resolution scans, 96 incomplete baseline scans, 125 missing outcomes or age, and 29 failed segmentations—yield a final cohort of 841 included subjects, with a sub-cohort of 125 subjects having KPS data.

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Subject inclusion and exclusion flowchart. Abbreviations: MR, magnetic resonance; T1w, T1-weighted.

Since the date of diagnosis (referred to as “day zero”), 90.4% (n = 761) of patients were deceased (the event), and 9.5% (n = 80) were lost to follow-up (censored). These patients were classified into 3 groups based on their survival duration: 16% were long-term survivors (LTS; n = 136), who lived for 730 days (2 years) or more; 57.5% were medium-term survivors (MTS; n = 484), who lived between 183 to 729 days (6 months to 2 years); and 26.3% were short-term survivors (STS; n = 221), who lived for less than 183 days (less than 6 months).

Data Collection and Processing

Clinical and molecular data, including age, sex, preoperative KPS, extent of resection (EOR), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, IDH1 mutation status, and overall survival, were collected for all patients. Preoperative MR imaging with T1-weighted, T1-weighted postcontrast, T2-weighted, and Fluid-Attenuated Inverse Recovery (FLAIR) sequences were collected. While acquisition parameters such as scanner strength, voxel size, slice thickness, and sequence protocols varied across institutions, a minimum slice thickness cutoff of 4.5 mm on the T1-weighted images was used to ensure robust segmentation of brain reserve parameters.

MR images were converted to the Neuroimaging Informatics Technology Initiative format and resampled to an isotropic resolution of 1 mm³. The data were then preprocessed following the Brain Tumor Segmentation (BraTS) protocol25,26 and the Cancer Imaging Phenomics Toolkit pipeline.27 Preprocessing involved image registration to a standard anatomical space (SRI24 atlas),28 bias correction, noise reduction, skull-stripping, and automatic segmentation of various tumor regions based on BraTS criteria, including enhancing tumor, necrotic region, and edema volumes. Tumor regions-of-interest and SRI24/LPBA40 (LONI Probabilistic Brain Atlas) parcellation maps28 were utilized to measure tumor volumes across various brain regions. Total tumor volume and FLAIR-based edema were employed to determine the tumor epicenter and laterality. In contrast to tumors located in the right, left, or bilateral hemispheres, tumors located in the brainstem and cerebellum were considered to have no laterality. Figure 2 provides an overview of the image processing pipeline.

Figure 2.

Pipeline diagram illustrating two parallel workflows merging into a single ‘Tumor-Based Morphometry Value Estimation’ output. The top workflow depicts the Cancer Imaging Phenomics Toolkit pipeline processing preoperative MR images for tumor ROI segmentation and volume ranking. The bottom workflow depicts the Computational Anatomy Toolbox 12 pipeline processing T1w images for voxel- and surface-based morphometry segmentation and ROI value extraction.

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Brain tumor segmentation and brain morphometry pipeline. Abbreviations: CaPTk, Cancer Imaging Phenomics Toolkit; CAT-12, Computational Anatomy Toolbox version 12; FLAIR, fluid-attenuated inversion recovery; LPBA40, LONI Probabilistic Brain Atlas; ROI, region of interest; SBM, surface-based morphometry; T1w, T1-weighted; T1w-Gd, T1-weighted postgadolinium contrast; T2w, T2-weighted.

Voxel-Based and Surface-Based Morphometry

Voxel-based and surface-based morphometry analyses were performed on T1-weighted MR images using CAT12 (Computational Anatomy Toolbox; Structural Brain Mapping Group) in MATLAB (version R2022b, MathWorks). The segmentation process involved: noise reduction, bias correction, affine registration to MNI152 (Montreal Neurological Institute) space, volumetric segmentation, skull-stripping, and regional parcellation.21 A homogeneity check was performed to verify the quality of the segmentation (Table S1). Voxel-based and surface-based morphometry metrics were derived from regional parcellations of LPBA40 (LONI Probabilistic Brain Atlas) and Destrieux atlases.21 Surface-based morphometry parameters, including gyrification, cortical thickness, and sulcal depth, were obtained by averaging morphometry measurements from various brain regions. Gyrification, cortical thickness, and sulcal depth are defined as the absolute mean curvature of the central surfaces, the distance between white matter and the pial surface, and the distance between the central surface and the cerebral hull, respectively (Figure 3).21 Among voxel-based morphometry values, gray matter volumes were calculated by summing the tissue volumes across different brain regions (Figure 4). The LPBA40 atlas does not parcellate deep white matter brain regions; therefore, white matter volumes were not included for analysis. To minimize tumor-related bias in the morphometric analysis, a 2-step process was implemented for all patients. First, all morphometric values within tumor-affected regions were excluded (Figure 4). Second, the term residual functional gray matter (rfGM) was introduced to represent the volume of healthy, tumor-free gray matter that remained available to support cognitive performance. The percentage of rfGM was determined by dividing the total gray matter volume of unaffected regions by the total intracranial volume and multiplying by 100.

Figure 3.

Seven-panel visual layout (A–G). Panels A and B display axial T1w post-contrast and FLAIR MRI slices of a right temporal glioblastoma. Panels C through F present axial and cortical brain maps of gray matter volume, gyrification, sulcal depth, and cortical thickness. Panel G presents a schematic cross-section detailing geometric measurements for cortical thickness, sulcal depth relative to the outer hull, and central surface curvature for gyrification.

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Structural and surface morphometry images of a 58-year-old right-handed female with a right temporal glioblastoma. The panel illustrates: (A) an axial T1-weighted postcontrast image, (B) an axial fluid-attenuated inversion recovery (FLAIR) image. Images (C-F) display patient-specific morphometric maps: (C) gray matter volumetric segmentation, (D) gyrification (mm), (E) sulcal depth (mm), and (F) cortical thickness (mm). (G) A schematic anatomical depiction of brain-reserve metrics. Gyrification measured as the regional absolute mean curvature of the central surface (dashed circles); sulcal depth measured as the distance between the deep cortical surface and the cerebral outer hull (long arrow); and cortical thickness measured as the distance between the white matter and pial surface (short arrow).

Figure 4.

Workflow diagram merging MRI tumor overlays and gray matter maps into a masked brain image with the tumor region shown in white. An adjacent table lists tumor and residual gray matter volumes across 10 brain regions, marking tumor-affected regions with “E” for eliminated.

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Selective elimination of brain reserve parameters based on tumor location in a 58-year-old patient. The patient presents with a dominant glioblastoma lesion in the right temporal lobe and an additional focus in the left frontal region, as delineated by fluid-attenuated inversion recovery (FLAIR) hyperintensity. Tumor segmentation ROI were generated, and tumor volumes across brain regions were statistically calculated. Morphometric values were selectively eliminated* (E*) from tumor-affected brain regions as depicted in the table.

Statistical Analysis

Multiple imputations were utilized to address missing clinical variables, including MGMT promoter methylation and EOR. Due to a high proportion of missing KPS data, it was neither imputed nor included in the imputation process to prevent bias and uncertainty from being introduced into the dataset. The imputation process was repeated 20 times, generating 10 datasets, and Rubin’s rule was applied to combine parameter estimates and SEs.29

Descriptive statistics were used to summarize patient characteristics. A chi-square goodness-of-fit test was utilized to examine categorical differences, while the Kruskal–Wallis test was employed to analyze differences in continuous variables. Both univariable and multivariable Cox proportional hazards models were used to assess the relationships between time-to-event endpoints and independent variables. Multivariable Cox analysis was performed on data with available KPS scores with inverse-probability weighting to account for differences in parameter distribution across the total data and the KPS subset. Additionally, survival analyses were conducted on subsets of elderly and nonelderly patients (those under 65 years) to examine the age-specific influence of brain reserve parameters. The age cutoff for categorizing elderly and nonelderly patients was determined based on prior studies.19,30 The degree and significance of the effect of brain-reserve metrics on survival outcomes were further established via a Wald test. The proportional hazards assumption was visually represented using Schoenfeld residual plots. A P-value of less than .05 was considered statistically significant. All analyses were conducted using R (version 4.3.2, R Foundation for Statistical Computing) and MATLAB (version R2022b, MathWorks). Data reporting for this observational study adhered to the STROBE guidelines.

Results

Data Distribution

We analyzed a total of 841 patients with IDH1-WT, high-grade glioblastoma. The median age at diagnosis was 64 years (range: 20-91 years), with 58.0% (n = 488) of patients being male and 42% (n = 353) female. A higher proportion of patients, specifically 39.8% (n = 335), had an unmethylated MGMT promoter, compared to 28.7% of methylated subjects. Additionally, 45.3% (n = 381) of the patients underwent gross total resection, and 41.3% (n = 347) had left-lateral tumors. Karnofsky Performance Status was available for 14.8% (n = 125) of the patients, with 8.4% (n = 71) having a score greater than 70. The median overall survival for the entire cohort was 368 days (range: 1-3023 days; Table S2).

Tumor Parameter Distribution

Across the imputed datasets, LTS (n = 136) were diagnosed at a significantly younger age (P < .001) in comparison to MTS (n = 484) and STS (n = 221). A larger proportion of LTS had methylated MGMT (P < .001), higher preoperative KPS (>70; P < .001), gross total resection (P < .001), and left-lateral tumors (P = .02) compared to the MTS and STS groups (Table 1). The Kruskal–Wallis test indicated significantly higher rfGM percentage (P < .001) in the LTS group compared to MTS and STS (Table 1). In contrast, LTS and MTS exhibited a similar degree of gyrification (P < .001) and cortical thickness (P < .001), which were significantly higher than in the STS group.

Table 1.

Chi-squared goodness-of-fit test and Kruskal–Wallis analysis from an example imputed total dataset with pooled P-values

Parameter Long-term survivors Medium-term survivors Short-term survivors Pooled P-value
(n = 136) (n = 484) (n = 221)
% % %
Sex
 Male 64.7 57.3 55.4 .2
 Female 35.3 42.7 44.6
MGMT
 Unmethylated 33.8 62.7 61.7 <.001
 Methylated 66.2 37.3 38.3
KPS
 High KPS 90 64.4 16.7 <.001
 Low KPS 10 35.6 83.3
EOR
 GTR 69.1 59.8 38.7 <.001
 STR 24.3 31.1 45
 Biopsy/LITT 6.6 9.1 16.2
Laterality
 Left 41.9 41 41.4 .02
 Right 38.2 37.1 27.5
 Bilateral 16.9 19.7 29.7
 Other 2.9 2.3 1.4
Medians
 Age (years) 61.8 62.5 70.8 <.001
Tumor volumes (mL)
 Enhancing 8.1 7.2 8.4 .2
 Necrosis 2.3 2.1 1.7 .24
 Edema 24.8 21.6 20.1 .11
Brain reserve parameters
 rfGM (%) 10.5 10.2 9.2 <.001
 GY (mm) 28.5 28.5 28.2 <.001
 CT (mm) 2.3 2.3 2.2 <.001
 SD (mm) 11.8 11.7 12 .06
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Abbreviations: CT, cortical thickness; EOR, extent of resection; GTR, gross total resection; GY, gyrification; KPS, Karnofsky Performance Status; LITT, laser interstitial thermal therapy; MGMT, O⁶-methylguanine-DNA methyltransferase; rfGM, residual functional gray matter volume; SD, sulcal depth; STR, subtotal resection; TIV, total intracranial volume.

Subgroup analysis by age revealed significant differences in parameter distributions between elderly (n = 396) and nonelderly (n = 445) patients. Among the elderly set, a higher proportion of LTS had a methylated MGMT promoter (P < .001), a higher KPS score (P < .001), and underwent gross total resection (P = .02). Additionally, both LTS and MTS exhibited significantly higher rfGM volumes (P < .001) and higher gyrification (P = .01) (see Table S3). Similarly, a significantly higher proportion of nonelderly LTS showed a methylated MGMT promoter (P < .001), had a KPS above 70 (P < .001), and underwent gross total resection (P < .001). Nonelderly LTS and MTS patients had significantly higher rfGM (P < .001) and lower sulcal depth (P = .03) compared to the STS subset (see Table S4).

Effect of Brain Reserve Parameters on Mortality Rates

The univariable Cox proportional hazards analysis indicated that higher rfGM (P < .001), gyrification (P = .02), cortical thickness (P < .001), and lower sulcal depth (P = .01) were associated with decreased mortality risk (Table S5 and Figure S1). Additionally, clinical variables such as younger age (P < .001), higher KPS (P < .001), methylated MGMT promoter (P < .001), undergoing gross total resection instead of biopsy (P < .001), and having unilateral tumors compared to bilateral tumors (P < .001) were also significantly associated with reduced mortality risk (Table S5).

In a multivariable Cox proportional hazards analysis (n = 841), reduced mortality was observed in patients with higher rfGM (P < .001) and higher gyrification (P = .008) after controlling for clinical parameters except age and KPS. All clinical parameters identified in the univariable analysis retained their significance in the multivariable analysis. (Table 2). A subset analysis was performed, controlling for age and KPS (n = 125), along with inverse-probability weighting to account for differences in parameter distribution between the total and the KPS dataset (Table S6). In this analysis, a significant decrease in mortality risk was associated with younger age (P = .04), higher KPS (above 70; P < .001), sex (male; P = .02), methylated MGMT promoter (P < .001), undergoing gross total (P < .001) and subtotal (P < .001) resection, higher gyrification (P = .01), higher cortical thickness (P = .005), and smaller sulcal depth (P = .02) (Table 2). Wald test statistics have shown significant improvement in Cox model performance with brain health metrics in comparison to a model without them (Table S7). A multivariable Cox proportional hazards analysis, adjusted for study site, confirmed that rfGM and gyrification retained their significant associations with mortality risk (Table S8).

Table 2.

Multivariable Cox proportional hazards analysis of overall survival in total data and KPS subset with inverse-probability weighting

Total dataset (no age and KPS)
 Subset data (with age and KPS)
n = 841
 n = 125
Parameter Reference category HR (95% CI) P-value HR (95% CI) P-value
Age — — — 1.02 (1.00-1.04) .04
KPS High KPS
 Low KPS — — 4.41 (2.60-7.49) <.001
Sex Female
 Male 1.00 (0.84-1.18) .97 0.60 (0.39-0.93) .02
MGMT Methylated
 Unmethylated 1.55 (1.31-1.83) <.001 2.40 (1.46-3.95) <.001
EOR Biopsy/LITT
 GTR 0.59 (0.46-0.77) <.001 0.24 (0.13-0.44) <.001
 STR 0.82 (0.62-1.08) .16 0.23 (0.11-0.44) <.001
Laterality Bilateral
 Left 0.75 (0.61-0.92) .006 0.67 (0.39-1.13) .13
 Right 0.80 (0.65-0.99) .03 1.24 (0.71-2.15) .44
 Other 0.71 (0.42-1.20) .21 0.69 (0.21-2.21) .54
Tumor volumes —
 Enhancing 1.00 (0.99-1.00) .002 1.00 (0.99-1.00) .88
 Necrosis 0.99 (0.99-1.00) .006 0.99 (0.99-1.00) .17
 Edema 0.99 (0.99-1.00) .001 0.99 (0.99-1.00) .96
Brain reserve parameters —
 Total GM/TIV (%) 0.98 (0.98-0.99) <.001 1.00 (0.99-1.01) .51
 GY 0.83 (0.73-0.95) .008 0.71 (0.54-0.94) .01
 CT 0.83 (0.64-1.16) .29 0.31 (0.12-0.69) .005
 SD 1.08 (0.98-1.19) .1 1.27 (1.02-1.58) .02
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Abbreviations: CT, cortical thickness; EOR, extent of resection; GM, gray matter volume; GTR, gross total resection; GY, gyrification; KPS, Karnofsky Performance Status; LITT, laser interstitial thermal therapy; MGMT, O⁶-methylguanine-DNA methyltransferase; SD, sulcal depth; STR, subtotal resection; TIV, total intracranial volume.

In the elderly subset, the univariable Cox proportional hazards analysis demonstrated a significant association among rfGM (P < .001), cortical thickness (P = .01), and sulcal depth (P = .01) with survival outcomes (Table S9, Figure S2, and  Figure 3). A multivariable analysis of elderly data retained significant associations between higher rfGM percentage (P < .001) and gyrification (P = .04) with lower mortality risk. The nonelderly subset gained a significant association between rfGM (P < .001) and mortality risk in the multivariable time-to-event analysis (Table S10).

Discussion

While the utility of brain-reserve metrics is well established in neurodegenerative disease research, their application to glioblastoma prognostication and treatment planning remains relatively underexplored. In this study, we demonstrate that brain-reserve metrics differ significantly across survival groups, supporting their potential prognostic utility in glioblastoma assessment. Gyrification, cortical thickness, and sulcal depth showed associations with overall survival, while the predictive value of rfGM volume was attenuated after adjusting for age and performance status, reflecting the well-recognized interdependence between these factors. Notably, the Wald test indicated that incorporating brain health metrics significantly enhanced the Cox models, suggesting that these metrics provide critical, independent prognostic information.

In this study, several established clinical and molecular prognosticators differentiated LTS from other surviving groups, with notable age-related differences in the contribution of brain reserve. In both elderly and nonelderly patients, LTS demonstrated a significantly higher prevalence of MGMT promoter methylation, better functional status, and higher rates of gross total resection, consistent with prior literature on glioblastoma prognostication.17,31 Elderly LTS and MTS exhibited greater rfGM volume and gyrification, whereas nonelderly LTS and MTS showed increased rfGM and reduced sulcal depth compared to STS. These findings suggest that the influence of brain reserve differs across aging populations. Accordingly, in addition to established prognostic factors such as age and performance status, brain-reserve metrics may function as dynamic biomarkers. By capturing neurological health at a given time point, these measures could help personalize treatment strategies in glioblastoma.

When examining mortality risk, both structural brain-reserve features and established clinical predictors demonstrated significant associations with survival in glioblastoma. Higher rfGM, gyrification, and cortical thickness, along with lower sulcal depth, were linked to reduced mortality in univariable Cox models. While favorable clinical characteristics, including younger age, higher KPS, MGMT promoter methylation, and greater EOR, were strongly associated with improved survival across all models, several brain health metrics, particularly surface-based morphometrics, remained significant after adjusting for key clinical factors. Wald tests and subset multivariable analyses—controlling for age and KPS—demonstrated the independent prognostic value of brain reserve-related morphometrics. We also observed age-specific influences of these parameters: while higher rfGM volume was significantly associated with mortality risk across both cohorts, cortical thickness was a significant predictor only in elderly patients, whereas sulcal depth was significant only in the nonelderly group. These findings suggest that while classic clinical and molecular markers remain dominant predictors of outcome,17,31 brain-reserve characteristics may provide additional prognostic insight while also accounting for age and performance of the individual.

There is a strong correlation between brain-reserve metrics and age, but this relationship is not always linear. For instance, the decline in gyrification is more pronounced during childhood and adolescence, followed by a steady, slow decline in the elderly.32,33 Higher gyrification indicates better cognitive health in the elderly population,34 which explains the better survival outcomes seen in patients with higher gyrification in our study. Our results on cortical thickness and gray matter volume are also aligned with the prior understanding of associations between these parameters and cognition.35 Interestingly, the association between sulcal depth and glioblastoma outcomes observed in our cohort contrasts with the known literature. Generally, higher sulcal depth is seen in younger individuals36,37 and is associated with working memory, reasoning, and other cognitive functions.38,39 A study by Miller et al. found regional sulcal depth variations across various brain regions to be related to the myelin content and resting-state connectivity profile.40 Furthermore, it is crucial to recognize that sulcal architecture is intrinsically complex, and morphometric alterations do not occur linearly or uniformly across the cortex.36 Cortical folding is hierarchically organized into primary, secondary, and tertiary sulci, each possessing distinct developmental trajectories and structural vulnerabilities.40 In this study, we relied on a global average of sulcal depth, which limits our insight into region-specific variations. While our study indicates that overall greater sulcal depth is associated with increased mortality, investigations exploring localized alterations in sulcal width, alongside distinct assessments of primary, secondary, and tertiary sulcal depths, are warranted. Such granular metrics could shed light on the precise interplay between cognitive load, structural neuroplasticity, and survival outcomes.

Among the imaging-based parameters of brain reserve, gray matter volumes and cortical thickness have received brief attention in prior glioma literature. Significant cortical thinning has been associated with poor survival, with a survival prediction accuracy above 75% in high-grade gliomas.12 While studies looking at the relationship between gray matter volume and survival are lacking, multiple papers have showcased structural morphometric changes in the contralesional hemisphere in a regional or hemispheric pattern.13,41 For instance, a study by Hu et al. reported an increase in inferior temporal gray matter,41 while Yuan et al. found a decrease in medial temporal gray matter in the contralesional hemisphere of glioma patients with unilateral temporal tumors compared to healthy controls.13 In addition to tumor location, contralateral gray matter volume can also be affected by tumor grade, with lower-grade and higher-grade gliomas potentially undergoing different mechanisms driven by the natural history of the underlying tumor type.42 Our study is distinct from prior literature in its methodology, with the use of a comprehensive panel of structural brain reserve parameters, a focus on survival outcomes while accounting for key clinical variables, analysis of a large population of glioblastoma patients as per the recent WHO classification, and a heterogeneous sample derived from multiple institutions. Additionally, rather than analyzing region-specific volumes and surface-based parameters, we computed morphometry averages for these parameters from healthy brain after eliminating values within tumor-affected regions, enabling a more realistic estimation of available brain reserve that contributes to patients outcomes.

Beyond age-related and comorbidity-associated volume loss, glioblastoma patients experience accelerated parenchymal atrophy due to chemotherapy and radiation; higher doses and larger treatment fields typically exacerbate this decline.43,44 While the influence of baseline brain reserve on the rate and severity of this treatment-related deterioration remains poorly understood, it is reasonable to hypothesize that patients with lower reserve are more vulnerable to pronounced decline. Consequently, evaluating both baseline brain reserve and its longitudinal trajectory could provide critical insights into survival patterns. For instance, Lorimer et al. investigated whether baseline imaging markers could predict acute radiotherapy-related side effects in older glioblastoma patients (>65 years). Their findings demonstrated that lower relative brain volume was significantly associated with a steeper decline in quality of life at 8 weeks, a higher likelihood of treatment nonattendance due to morbidity or death, and reduced overall survival.43 The current study, supported by existing literature, underscores the significant role of brain-reserve metrics in glioblastoma survival outcomes. Presently, treatment courses are largely determined by age and performance status45—parameters increasingly recognized as insufficient for comprehensive patient assessment.46 The quantifiable morphometric brain-reserve metrics offer a unique opportunity to serve as biomarkers for personalized treatment strategies.

Impaired cognitive and brain reserve have been observed in various oncological disorders both before and after systemic therapies. For example, in patients with brain metastases, breast cancer, and leukemia, increased brain volume loss correlates with reduced survival.6,7,47 Recently, automated techniques such as FreeSurfer, FSL, SPM, and CAT12 have proven sensitive enough to detect localized gray matter decreases at the individual patient level.48 However, clinical implementation remains limited. While new software packages provide reliable volume extraction in a clinical setting,49 there is a clear need for well-validated, neuro-oncology-specific morphometry tools to translate these findings into routine practice.

To our knowledge, this study is the first to comprehensively evaluate morphometric parameters within a multi-institutional glioblastoma cohort, establishing a robust framework linking structural brain reserve to survival outcomes. Importantly, our findings highlight a potential biomarker that can aid clinical decision-making along with the traditional parameters. While the direct clinical translation of these metrics is the ultimate goal, we acknowledge that further prospective studies and specialized, oncology-specific automated extraction tools are required before these parameters can be integrated into routine workflows. We propose that MRI-based brain-reserve metrics represent a promising tool for assessing neurological and physiological resilience, not only in neuro-oncology but potentially across all malignancies. A deeper understanding of these parameters could significantly enhance prognostication, quality-of-life predictions, and individualized treatment planning.

Limitations

A notable limitation of this study is the extensive missing data regarding KPS within the larger dataset. Performance status is a well-established predictor of glioblastoma survival and correlates strongly with brain-reserve metrics. Consequently, evaluating the combined effect of KPS and brain reserve on survival outcomes across the entire cohort was not feasible. This missingness introduces the potential for unmeasured confounding; specifically, the prognostic effect of the morphometric parameters may be overestimated in the full dataset when not adjusted for age and KPS. Although we attempted to mitigate this by conducting a subset Cox proportional hazards analysis with inverse-probability weighting on the available KPS data—which still demonstrated a significant improvement in the prediction model via Wald test statistics—the inability to control for performance status across the complete dataset remains a constraint on our primary findings. Additionally, detailed information regarding patients’ comorbidities and treatment history that could influence patient survival outcomes was unavailable. Given that most patients in the consortium data underwent the Stupp protocol, there is potential for selection bias, as these individuals may have had higher preoperative KPS values. While the effects of actual tumor growth and edema on brain reserve parameters were addressed, the impact of mass effect on the adjacent lobe, midline shift, and baseline cognitive reserve were not considered in our analysis.

Conclusion

This study represents the first multi-institutional investigation that has extensively examined the prognostic significance of brain-reserve metrics in IDH1-WT glioblastoma patients. Imaging-based brain-reserve metrics may offer prognostic value beyond age and KPS, thereby expanding the biomarker repertoire for glioblastoma. Additional research is warranted to elucidate the role of brain reserve parameters in guiding treatment strategies, particularly among elderly patients.

Supplementary Material

vdag241_Supplementary_Data
vdag241_supplementary_data.pdf (801.7KB, pdf)

Acknowledgments

The work was presented at the 111th Scientific Assembly and Annual Meeting of the Radiology Society of North America under the title “Effect of Baseline Brain Health on Survival Outcomes in Glioblastoma Patients” and was awarded Trainee Research Prize.

Contributor Information

Sree Gongala, Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland.

Robin Ghotra, Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland.

George Wang, School of Medicine, Case Western Reserve University, Cleveland.

Jose A Garcia, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia; Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.

Rubo Xing, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.

Parisa Arjmand, Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland; School of Medicine, Case Western Reserve University, Cleveland.

Pamela LaMontagne, School of Medicine, Case Western Reserve University, Cleveland; Department of Radiology, Washington University School of Medicine, St. Louis.

Carmen Balana, Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Badalona.

Josep Puig, Research Unit (IDIR) Image Diagnosis Institute, Badalona.

Danial Marcus, Department of Radiology, Washington University School of Medicine, St. Louis.

Yilun Sun, Department of Population and Quantitative Health Sciences, Case Western Reserve University and University Hospitals of Cleveland, Cleveland.

Wassim Malak, Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland.

Haley Perlow, Department of Radiation Oncology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland.

Tiffany R Hodges, Department of Neurological Surgery, Case Western Reserve University and University Hospitals of Cleveland, Cleveland; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland.

Christos Davatzikos, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia; Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia; Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.

Chaitra Badve, Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland; Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland.

Supplementary Material

Supplementary material is available online at Neuro-Oncology Advances (https://academic.oup.com/noa).

Author Contributions

S.G., R.G., T.R.H., C.D., C.Bad.: contributed to concept, design, and analysis; data acquisition; interpretation and writing; and drafting the final version. G.W., P.A., Y.S., H.P.: contributed to concept, design, and analysis; interpretation and writing; and drafting the final version. J.A.G., R.X., contributed to concept, design, and analysis; data acquisition; and drafting the final version. P.L., C.Bal., J.P., D.M.: contributed to concept, design, and analysis; and drafting the final version. Large language model called “Grammarly Gen AI” was used for grammatical corrections and spelling checks.

Conflict of Interest Statement

All authors declare no conflict of interest.

Funding

This work was supported by the Imaging Devices and AI Technologies Track Funding Agency: Jobs Ohio, National Institutes of Health/National Cancer Institute [1RO1CA269948-01 and 1RO1CA269604]. Institutional research support from Siemens Healthineers (S.G., C.Bad.) unrelated to this project.

Ethics Approval Statement

Approval from the Institutional Review Board was secured to collect data from University Hospitals Cleveland Medical Center. Due to the retrospective nature of the study, a waiver of consent was also obtained.

Data Availability

The data generated or analyzed during the study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

vdag241_Supplementary_Data
vdag241_supplementary_data.pdf (801.7KB, pdf)

Data Availability Statement

The data generated or analyzed during the study are available from the corresponding author upon reasonable request.

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