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

TL;DR

This study introduces R-DepTH, an MRI-based descriptor combining tissue deformation and texture analysis to better predict survival outcomes in Glioblastoma patients, reflecting tumor systemic effects beyond the visible tumor.

Contribution

The paper presents a novel integrated MRI descriptor, R-DepTH, that captures biomechanical tissue deformations and morphological heterogeneity to improve prognosis prediction in GBM.

Findings

R-DepTH significantly stratifies patients into risk groups with p-values < 0.005.

The descriptor improves survival prediction accuracy over existing methods.

It demonstrates potential as a prognostic marker for tumor aggressiveness.

Abstract

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure 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 grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity within the tumor confines, on MRI scans. R-DepTH, on N = 207 GBM cases (training set (St) = 128, testing set (Sv) = 79), demonstrated improved prognosis of overall survival by categorizing patients into low- (prolonged survival) and high-risk (poor survival) groups (on St, p-value = 0.0000035, and on Sv, p-value = 0.0024). R-DepTH descriptor may serve as a comprehensive MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.