No abstract

Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).

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.

Background and Purpose: Differentiating pseudo-progression (PsP), a radiation-induced treatment effect, from tumor progression on imaging is a significant challenge in Glioblastoma management. Unfortunately, guidelines set by RANO criteria are based solely on bidirectional diametric measurements of enhancement observed on T1w, T2w/FLAIR scans. We hypothesize that quantitative 3D shape features of the enhancing lesion on T1w, and T2w/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiological differences across PsP and tumor recurrence, not appreciable on diametric measurements alone. Materials and Methods: A total of 105 Glioblastoma studies from 2 institutions were analyzed, consisting of a training (N=59) and an independent test (N=46) cohort. For every study, expert delineation of the lesion habitat (T1w enhancing lesion and T2w/FLAIR hyperintense peri-lesional region) was obtained, followed by extracting 30 shape features capturing 14 “global” contour characteristics, and 16 “local” curvature measures, for every habitat region. Feature selection was employed to identify most discriminative features on the training cohort and were evaluated on the test cohort using a support vector machine classifier. Results: Top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion, and curvedness of T2w/FLAIR hyperintense peri-lesional region. Using top features from the training cohort (training accuracy=91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing PsP from tumor progression. Conclusion: Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across PsP and tumor progression and could be used to distinguish these radiographically-similar pathologies.