Most aggressive tumors are systemic, implying that their impact is not localized to the tumor itself but extends well beyond the visible tumor borders. Solid tumors (e.g. Glioblastoma) typically exert pressure on the surrounding normal parenchyma due to active proliferation, impacting neighboring structures and worsening survival. Existing approaches have focused on capturing tumor heterogeneity via shape, intensity, and texture radiomic statistics within the visible surgical margins on pre-treatment scans, with the clinical purpose of improving treatment management. However, a poorly understood aspect of heterogeneity is the impact of active proliferation and tumor burden, leading to subtle deformations in the surrounding normal parenchyma distal to the tumor. We introduce radiographic-Deformation and Textural Heterogeneity (r-DepTH), a new descriptor that attempts to capture both intra-, as well as extra-tumoral heterogeneity. r-DepTH combines radiomic measurements of (a) subtle tissue deformation measures throughout the extraneous surrounding normal parenchyma, and (b) the gradient-based textural patterns in tumor and adjacent peri-tumoral regions. We demonstrate that r-DepTH enables improved prediction of disease outcome compared to descriptors extracted from within the visible tumor alone. The efficacy of r-DepTH is demonstrated in the context of distinguishing long-term (LTS) versus short-term (STS) survivors of Glioblastoma, a highly malignant brain tumor. Using a training set (N = 68) of treatment-naive Gadolinium T1w MRI scans, r-DepTH achieved an AUC of 0.83 in distinguishing STS versus LTS. Kaplan Meier survival analysis on an independent cohort (N = 11) using the r-DepTH descriptor resulted in p = 0.038 (log-rank test), a significant improvement over employing deformation descriptors from normal parenchyma (p = 0.17), or textural descriptors from visible tumor (p = 0.81) alone.