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 progressionfree 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 ¼ 130), Ivy GAP (n ¼ 32), and Cleveland Clinic (n ¼ 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 ¼ 130) and evaluated on the holdout cohort (n ¼ 73). Further, Gene Ontology and singlesample 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 ¼ 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.

Technical Note: MRQy — An open‐source tool for quality control of MR imaging data

“…In addition to the original cohort (as downloaded from TCIA), a processed version of the TCGA‐GBM cohort was obtained from a publicly accessible release 21,22 . Briefly, processed MR volumes had been made available as NIFTI files after undergoing the following steps: (a) re‐orientation to the left‐posterior‐superior coordinate system, (b) co‐registration to the T1w anatomical template of SRI24 Multi‐Channel Normal Adult Human Brain Atlas via affine registration, (c) resampling to voxel resolution, (d) skull‐stripping, and (e) de‐noising using a low‐level image processing smoothing filter, and intensity standardization to an image distribution template.…”

Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance

“…The radiomics risk score for the i-th patient is the summation of radiomics features multiplied by the corresponding coefficients derived from Lasso-Cox regression analysis, where n is the number of features selected by LASSO, β j is the j -th weighted coefficient of the selected feature, and X ij is the j -th selected radiomic features for i -th patient. This method has been widely used in many radiomics studies encompassing various tumor types [ 19 , 20 , 21 , 22 ].…”

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

“…Risk parameters yielding negative regression coefficients (ie, low feature values correlated with long-term survival) produce a HR between 0 and 1; features yielding positive regression coefficients (ie, low feature values correlated with short-term survival) produce a HR between 1 and infinity. 40 , 84 , 85 …”

“…For example, radiogenomic approaches have been used to create “virtual-biopsy” maps to predict specific prognostic point mutations as well as chromosomal alterations 37 based on the 2016 update on the WHO classification of diffuse gliomas in neuro-oncology. 13 , 38 , 39 Identifying such radiogenomics associations may improve our understanding of how the changes in biological processes at the molecular level affect changes at a radiologic scale 40 and may ultimately aid in personalizing treatment decisions.…”