Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma
Year: 2020
Type: article
Study design: Observational Study
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.
Source: Clinical Cancer Research
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Systems biology frameworks to unravel mechanisms driving complex disorders, Pathology Image Informatics Platform for visualization, analysis and management, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, Decision Support System for Predicting outcome of ER+ breast cancers, CHIRP Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung Cancers, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, Computerized histologic image predictor of cancer outcome, Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal Carcinoma, Case Comprehensive Cancer Center (Case CCC) Cancer Health Disparities SPORE Planning Grant, G86714149, Software to facilitate multimode, multiscale fused data for Pathology and Radiolo, Computational Genomic Epidemiology of Cancer (CoGEC) Training Program
OpenAlex ID: W3006883269