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

arXiv: https://arxiv.org/abs/2103.07423 · HTML: https://mlsanity.com/paper/2103.07423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 56 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)

## Built on by (most cited first, 3 of 3)

- [Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images](https://mlsanity.com/paper/2109.12334/) (2021)
- [Graph-Radiomic Learning (GrRAiL) Descriptor to Characterize Imaging Heterogeneity in Confounding Tumor Pathologies](https://mlsanity.com/paper/2509.19258/) (2025)
- [The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information](https://mlsanity.com/paper/2507.15548/) (2025)

## Abstract (arXiv)

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.