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The concept of tumor field effect implies that cancer is a systemic diseasewith its impact way beyond the visible tumor confines. For instance, inGlioblastoma (GBM), an aggressive brain tumor, the increase in intracranialpressure due to tumor burden often leads to brain herniation and poor outcomes.Our work is based on the rationale that highly aggressive tumors tend to growuncontrollably, leading to pronounced biomechanical tissue deformations in thenormal parenchyma, which when combined with local morphological differences inthe tumor confines on MRI scans, will comprehensively capture tumor fieldeffect. Specifically, we present an integrated MRI-based descriptor,radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptorcomprises measurements of the subtle perturbations in tissue deformationsthroughout the surrounding normal parenchyma due to mass effect. This involvesnon-rigidly aligning the patients MRI scans to a healthy atlas viadiffeomorphic registration. The resulting inverse mapping is used to obtain thedeformation field magnitudes in the normal parenchyma. These measurements arethen combined with a 3D texture descriptor, Co-occurrence of Local AnisotropicGradient Orientations (COLLAGE), which captures the morphological heterogeneitywithin the tumor confines, on MRI scans. R-DepTH, on N = 207 GBM cases(training set (St) = 128, testing set (Sv) = 79), demonstrated improvedprognosis of overall survival by categorizing patients into low- (prolongedsurvival) and high-risk (poor survival) groups (on St, p-value = 0.35, andon Sv, p-value = 0.0024). R-DepTH descriptor may serve as a comprehensiveMRI-based prognostic marker of disease aggressiveness and survival in solidtumors.

Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to  characterize Tumor Field Effect: Application to Survival Prediction in  Glioblastoma