Abstract
BACKGROUND AND PURPOSE: Glioblastoma is an aggressive brain tumor, with no validated prognostic biomarkers for survival before surgical resection. Although recent approaches have demonstrated the prognostic ability of tumor habitat (constituting necrotic core, enhancing lesion, T2/FLAIR hyperintensity subcompartments) derived radiomic features for glioblastoma survival on treatment-naive MR imaging scans, radiomic features are known to be sensitive to MR imaging acquisitions across sites and scanners. In this study, we sought to identify the radiomic features that are both stable across sites and discriminatory of poor and improved progression-free survival in glioblastoma tumors. MATERIALS AND METHODS: We used 150 treatment-naive glioblastoma MR imaging scans (Gadolinium-T1w, T2w, FLAIR) obtained from 5 sites. For every tumor subcompartment (enhancing tumor, peritumoral FLAIR-hyperintensities, necrosis), a total of 316 three-dimensional radiomic features were extracted. The training cohort constituted studies from 4 sites (n = 93) to select the most stable and discriminatory radiomic features for every tumor subcompartment. These features were used on a hold-out cohort (n = 57) to evaluate their ability to discriminate patients with poor survival from those with improved survival. RESULTS: Incorporating the most stable and discriminatory features within a linear discriminant analysis classifier yielded areas under the curve of 0.71, 0.73, and 0.76 on the test set for distinguishing poor and improved survival compared with discriminatory features alone (areas under the curve of 0.65, 0.54, 0.62) from the necrotic core, enhancing tumor, and peritumoral T2/FLAIR hyperintensity, respectively. CONCLUSIONS: Incorporating stable and discriminatory radiomic features extracted from tumors and associated habitats across multisite MR imaging sequences may yield robust prognostic classifiers of patient survival in glioblastoma tumors.