Abstract PURPOSE The use of immunotherapy in glioblastoma management is under active investigation. Glioblastomas are “cold” tumors, meaning that they have inactivated or fewer tumor infiltrative lymphocytes in addition to substantial tumor necrosis, attributing to their poor response to immunotherapy. A significant challenge is the apriori identification of Glioblastoma patients who will respond favorably to immunotherapy. In this work, we evaluated the ability of computerized MRI-based quantitative features (radiomics) extracted from the lesion habitat (including enhancing lesion, necrosis, and peritumoral hyperintensities) to predict response and progression-free survival (PFS) in recurrent GBM patients treated with combination of Nivolumab and Bevacizumab. METHODS Immunotherapy response assessment in neuro-oncology (iRANO) criteria along with PFS were used to analyze n=50 patients enrolled in a randomized clinical trial where patients received Nivolumab with either standard or low dose Bevacizumab. These patients were assessed to see if they had complete response, partial response, stable disease (i.e. responders, n=31), or disease progression (i.e. non-responders, n=19). Lesion habitat constituting necrotic core, enhancing tumor, and edema were delineated by expert radiologist on Gd-T1w, T2w and FLAIR MRI scans. COLIAGE radiomic features from each of the delineated regions were selected using minimum redundancy maximum relevance (mRMR) via cross-validation, to segregate non-responder patients from responders. A multivariable cox proportional hazard model was used to predict survival (PFS). RESULTS CoLlAGe correlation, sum average, and sum variance features (capture local heterogeneity) from the lesion habitat, were found to segregate non-responder patients from responders with an accuracy of 86%, followed by 80% using features from peritumoral hyperintensities and 78% from enhancing tumor. In our survival analysis, C-index of 0.688 was obtained using features from the entire lesion habitat, followed by peritumoral hyperintensities (0.675) and enhancing tumor (0.656). CONCLUSION Radiomic features from the lesion habitat may predict response to combination of Nivolumab and Bevacizumab in recurrent Glioblastomas.