Radiomics-based survival risk stratification of glioblastoma is associated with different genome alteration

Abstract

Background: Glioblastoma (GBM) is a remarkable heterogeneous tumor with few non-invasive, repeatable, and cost-effective prognostic biomarkers reported. In this study, we aim to explore the association between radiomic features and prognosis and genomic alterations in GBM.

Methods: A total of 180 GBM patients (training cohort: n = 119; validation cohort 1: n = 37; validation cohort 2: n = 24) were enrolled and underwent preoperative MRI scans. From the multiparametric (T1, T1-Gd, T2, and T2-FLAIR) MR images, the radscore was developed to predict overall survival (OS) in a multistep postprocessing workflow and validated in two external validation cohorts. The prognostic accuracy of the radscore was assessed with concordance index (C-index) and Brier scores. Furthermore, we used hierarchical clustering and enrichment analysis to explore the association between image features and genomic alterations.

Results: The MRI-based radscore was significantly correlated with OS in the training cohort (C-index: 0.70), validation cohort 1 (C-index: 0.66), and validation cohort 2 (C-index: 0.74). Multivariate analysis revealed that the radscore was an independent prognostic factor. Cluster analysis and enrichment analysis revealed that two distinct phenotypic clusters involved in distinct biological processes and pathways, including the VEGFA-VEGFR2 signaling pathway (q-value = 0.033), JAK-STAT signaling pathway (q-value = 0.049), and regulation of MAPK cascade (q-value = 0.0015/0.025).

Conclusions: Radiomic features and radiomics-derived radscores provided important phenotypic and prognostic information with great potential for risk stratification in GBM.

Keywords: Glioblastoma; Magnetic resonance imaging; Prognosis; Radiomics; Unsupervised learning.