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. 2023 Apr 14;5(1):vdad034.
doi: 10.1093/noajnl/vdad034. eCollection 2023 Jan-Dec.

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival

Bidhan Lamichhane  1 , Patrick H Luckett  1 , Donna Dierker  2 , Ki Yun Park  1 , Harold Burton  3 , Michael Olufawo  1 , Gabriel Trevino  1 , John J Lee  2 , Andy G S Daniel  4 , Carl D Hacker  1 , Daniel S Marcus  2 , Joshua S Shimony  2 , Eric C Leuthardt  1   3   4   5   6   7   8
Affiliations

Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival

Bidhan Lamichhane et al. Neurooncol Adv. .

Abstract

Background: Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients.

Methods: We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival.

Results: Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%.

Conclusions: These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.

Keywords: biomarker of GBM and HGG; brain tumor; cortical thickness; glioblastoma and high-grade glioma; overall survival.

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Conflict of interest statement

E.C.L. has equity in Neurolutions, Inner Cosmos. E.C.L., D.S.M., and C.D.H. have equity in Sora Neuroscience. E.C.L, J.J.L., and J.S.S. have patent interests licensed to Sora Neuroscience. D.S.M. has equity in Radiologics, Inc. Washington University has equity in Neurolutions. B.L and E.C.L. have filed a provisional patent on the techniques described in this manuscript. All other authors declared that they had no conflicts of interest to their authorship or the publication of this article.

Figures

Figure 1.
Figure 1.
Visualization of differences in contralateral cortical thickness (CT; overlaid on human connectome project’s surface mesh, for visualization purpose). The cortical map summarizes the parcel’s CT. (A) The differences in lobar CT between groups (GBM/HGG-HC). (C) The differences in parcelwise CT between groups (GBM/HGG-HC). The negative value (blue) in both A and C represents the thinner cortex in patients compared to HC. (B and D) The plot of −log10 of the p-value of the group difference of lobar and parcel’s CT, respectively (showing only the parcels survived in permutation test). GBM/HGG, glioblastoma and high-grade glioma; HC, healthy control.
Figure 2.
Figure 2.
(A) Overall survival in patients with low cortical thickness (CT) (median CT; Wilcoxon rank-sum, W = 2120, p < .0001). (B) Kaplan–Meier survival analysis comparing overall survival in low CT patients and high CT patients (Chi2=13.42, p = .0003).
Figure 3.
Figure 3.
(A) Nested cross-validation classification accuracy of random forest models for classifying patients as short-term (ST) versus Long-term (LT) survivors based on median survival. The overall accuracy of the model was 83.3%. The model correctly classified ST with 89.7% accuracy and LT with 76% accuracy. (B) The top 10 strongest features used by the random forest for survival classification based on embedded feature selection. The strongest features included age and various brain regions at different scales.
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