Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Abstract

Purpose: To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways.

Experimental design: Two hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features.

Results: Twenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM.

Conclusions: Our findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM.

Figure 1:

(A) Flow diagram of patient enrolment, eligibility and exclusion criteria of the dataset (B) Radiomic Workflow

Figure 2:

(A) Radiomic feature selection using the least absolute shrinkage and selection operator (Lasso) cox regression model. Tuning penalization parameter lambda using 5-fold cross validation and minimum criterion in lasso model. The partial likelihood deviance was plotted versus log lambda. Log Lamda = −2.4308, with lambda = 0.0880 was chosen. (B) Forest plot of the beta coefficients/weights of the 25 radiomic features selected in the radiomic risk score. Brown, green, and yellow represent features obtained from the necrotic core, enhancing region and edema of the tumor habitat respectively, from Gd-T1w MRI. (C) Kaplan-Meier curves for patients stratified into low-risk and high-risk groups according to the radiomic risk score (cutoff = −0.1044) in the training cohort and independent validation set respectively. X-axis represents the progression free survival days in days, and Y-axis represents the estimated survival function.

Figure 3:

(A) Hierarchical clustering of the differentially expressing genes (n=192, p<0.03, false discovery rate = 3%) for radiomic risk score within the training cohort of 125 GBM patients. The genes were clustered based on the ‘correlation’ distance metric. (B) (left) Box-plot of the ID1 gene expression within the training cohort for the low-risk and high-risk radiomic groups. (right) Box-plot of the BMP4 gene expression within the training cohort for the low-risk and high-risk radiomic groups. High BMP4 expression is known to be associated with better prognosis. In consensus, we report that high BMP4 expression was found in the low radiomic risk group. (C) Gene Ontology analysis revealed several biological processes that were associated with the radiomic risk score. Fisher’s exact text (with Bonferroni correction for multiple testing of 5%), related the radiomic risk score of progression free survival with biological processes of cell differentiation, proliferation, angiogenesis and cell adhesion. (D) 2-D scatter plot to show the number of genes involved in each biological process.

Figure 4:

(A) Spearman rank correlation coefficient matrix of the single-sample Gene set enrichment analysis scores and the radiomic features extracted from the GBM tumor habitat (necrotic core, enhancing tumor and peri-tumoral edema). ‘*’ signifies a statistically significant (p<0.05) relationship (B) Top row – Gabor wavelet based radiomic features extracted from the peri-tumoral edema region of the GBM tumor habitat was found to be positively correlated with the cell differentiation and proliferation biological process. Bottom row – Shape feature ‘Elongation’ of the peri-tumoral edema was found to be positively correlated with angiogenesis and cell proliferation biological processes.