Over 40% of Glioblastoma (GBM) patients do not respond to conventional chemo-radiation therapy (chemo-RT) and relapse within 6-9 months, suggesting that they may have been better suited for other targeted therapies. Currently, there are no biomarkers that can reliably predict patients' response to chemo-RT in GBM. We seek to evaluate the role of radiomic markers on pre-treatment MRI to predict GBM patients' response to chemo-RT. Further, to establish a biological underpinning of the radiomic markers, we identified radiogenomic correlates of the radiomic markers with signaling pathways that are known to impact chemo-RT response. A total of 49 studies with Gd-T1w, T2w, FLAIR MRI protocols and corresponding gene expression were obtained from Ivy GAP (n=29) and TCIA (n=20) databases. Responders (n=22) were patients with progression-free survival (PFS) of at least ≥ 6 months, while non-responders (n=27) had PFS < 6 months. 13 molecular pathways were curated from the MSigDB Hallmark gene set. For each study, enhancing tumor on MRI was manually segmented by an expert reader. 1390 3D-radiomic features (Gabor, Haralick, and Laws energy) were extracted from this region across all MRI protocols. Joint mutual information identified the 3 most predictive radiomic features in the training set (n=29). This was followed by correlating these features with the gene set enrichment analysis (GSEA) score computed for every pathway. A support vector machine (SVM) classifier was trained using these 3 features and validated on a test set (n=20) that resulted in an Area Under Curve (AUC) of 0.71 to distinguish chemo-RT responders from non-responders. Laws energy descriptor (characterizing appearance of edges, spots, and ripples) from the enhancing region on Gd-T1w MR images were found to best predict chemo-RT response. Radiogenomic correlation with GSEA scores revealed that these radiomic features were significantly associated with PI3K/AKT/mTOR (promotes cell proliferation, survival) and apoptosis (programmed cell death) signaling pathways (p < 0.03, False Discovery Rate = 5%).

Date Published: 13 March 2019
PDF: 12 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501B (13 March 2019); doi: 10.1117/12.2512258

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)