Antiangiogenic treatment with bevacizumab is the single most widely used therapeutic agent for patients with recurrent glioblastoma (GB). A major challenge is that there are currently no validated biomarkers that can delineate molecular activity and predict treatment Response (1,2). Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical Imaging (3,4).A total of 4842 quantitative MRI features (including first-order, volume and shape features and texture features) were automatically extracted from the complete three-dimensional tumor bulk of 129 patients with recurrent GB prior antiangiogenic therapy following transformation of arbitrary MR signal into quantitative intensities (hybrid-white-stripe statistical normalization(5)), then screened for association with treatment outcome using bootstrap-resampling methods, and finally analyzed using unsupervised hierarchical clustering for radiomic signature discovery (Fig. 1). The utility of the obtained radiomic clusters on stratifying response to antiangiogenic therapy was modeled via Cox proportional hazards models with bootstrap resampling for internal validation.Leveraging a high throughput approach, we identified a radiomic signature consisting of 82 MRI features that segmented patients to three distinct consensus clusters (Fig. 2) which predict and stratify treatment response to antiangiogenic therapy by means of both progression-free and overall survival in patients with recurrent GB (Fig. 3). We furthermore provide evidence that the cluster of tumors with the most favorable treatment response arises from an anatomically distinct niche location.Our radiomic signature emerges as a putative imaging biomarker for predicting response to antiangiogenic therapy in patients with recurrent GB, advances the knowledge in the non-invasive analysis and characterization of brain tumours, and stresses the role of radiomics as a novel tool for improving decision-support in cancer treatment at low cost.