# Exploring Diverse Radiomics Approaches for Survival Prediction and Risk Stratification in Glioma Using MRI

Walid Dandachly, Benjamin Leporq, Frank Pilleul, Vincent Grégoire, Olivier Beuf

► To cite this version:

Walid Dandachly, Benjamin Leporq, Frank Pilleul, Vincent Grégoire, Olivier Beuf. Exploring Diverse Radiomics Approaches for Survival Prediction and Risk Stratification in Glioma Using MRI. 11th Annual Symposium on Magnetic Resonance in Radiation Therapy, Apr 2025, New York, NY, United States. hal-05218280

HAL Id: hal-05218280

https://hal.science/hal-05218280v1

Submitted on 21 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Exploring Diverse Radiomics Approaches for Survival Prediction and Risk Stratification in Glioma Using MRI

## Purpose

Radiomics has gained notable momentum in the field of oncology over the past decade, with its use as a decision support tool for clinical applications in cancer has emerged as a significant area of research. Our aim is to exploit radiomics for survival prediction and risk stratification in glioma, employing classical and novel approaches.

## Methods

The UPENN-GBM cohort form TCIA (The Cancer Imaging Archive) was utilized in this study. This dataset includes multiple contrast Magnetic Resonance Imaging (MRI) scans of 611 de novo glioblastoma patients from the university of Pennsylvania Health System, coupled with clinical and demographic data, including age, gender and survival information from first surgery. In addition, the automatic and manually corrected segmentation of distinct tumor sub-regions including: Tumor core, Enhancing tumor and Invasion was available for all patients.

Conventional and voxel-wise radiomics were extracted from each tumor sub-region and the entire tumor volume using PyRadiomics v3.1.0, with the default extraction parameters (binWidth: 25; resampledPixelSpacing: disabled; weightingNorm: none). For voxel-wise radiomics, two distinct approaches were utilized. In the first approach, the average voxel value was calculated for each feature. In the second approach, K-means clustering (K=3) was applied to features extracted from the entire tumor volume to generate distinct tumor habitats (figure 1), and the volume of each habitat was calculated. Pearson correlation analysis (correlation coefficient > 0.9) was applied to identify and eliminate highly correlated features. The remaining features, along with patient age, were then used as predictors in a Cox regression model.

To build the models, every possible combination of two and three predictors was evaluated. Four-fold cross-validation was employed to enhance the reliability of the results. Model performance was assessed using P-values and the concordance index (C-index). Patients were divided into low- and high-risk groups based on risk scores predicted by the Cox proportional hazards model.

## Results

Radiomics features were extracted using two different MRI acquisitions (gadolinium-enhanced T1- and T2-weighted images). Thousands of models were built, trained and tested based on the remaining features after the application of the Pearson correlation analysis.

The C-index for the best models ranged from 0.647 to 0.654 (95% CI: 0.534-0.751; P < .0001) in conventional radiomics and from 0.643 to 0.655 (95% CI: 0.530-0.753; P < .0001) in spatial radiomics. While for the habitats-based approach the C-index for the best models ranged from 0.635 to 0.639 (95% CI: 0.526-0.738; P < .0001) (figure 2).

## Conclusion
All radiomics approach offered comparable results, with the classical method having a slight edge on both
spatial radiomics approach, and the same can be said for the two weighting mechanisms. However, there
were differences in model's performance when features are extracted from the different tumor sub-
regions, which is mainly due to tumor heterogeneity. Although these results can be further improved by
including more predictors in the model, our goal was to keep the model as simple as possible which will
facilitate the reproducibility of generalization of the results.
<figure><img src="image_2.png" /><figcaption>Figure 1: Clustered radiomics feature.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 2: Kaplan-Meier curves for the best models: (a) consists of the following predictors (age, gldm large dependence high gray level emphasis, and shape sphericity) extracted from the invasion sub-region using T2-weighted images at different risks stratified, (b) consists of the following predictors (age, glcm correlation, and glcm inverse variance) extracted from the whole tumor using gadolinium-enhanced T1-weighted images, (c) consists of the following predictors (age, glszm large area emphasis habitat 1) extracted from the whole tumor using gadolinium-enhanced T1-weighted images, (d) consists of the following predictors (age, glcm sum squares habitat 1) extracted from the whole tumor using T2-weighted images</figcaption></figure>