# Novel radiomics approach for survival prediction and risk stratification in glioma using MRI

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

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Dandachly Walid, Leporq Benjamin, Pilleul Frank, Gregoire Vincent, Beuf Olivier. Novel radiomics approach for survival prediction and risk stratification in glioma using MRI. Société Française de Résonance Magnétique en Biologie et Médecine, Mar 2025, Saint-malo, France. hal-05233528

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Submitted on 1 Sep 2025

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# Novel radiomics approach for survival prediction and risk stratification in glioma using MRI

DANDACHLY Walid¹, LEPORQ Benjamin¹, PILLEUL Frank¹,², GREGOIRE Vincent³, BEUF Olivier¹

1. Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69100, LYON, France ; 2. CRLCC Léon Bérard – Département de Radiologie ; 3. CRLCC Léon Bérard – Département de Radiothérapie

## Introduction

Glioma is the most common and aggressive primary brain cancer, yet detecting prognostic biomarkers still requires invasive and costly methods. With its superior soft tissue contrast, MRI plays a crucial role in glioma diagnosis. Radiomics further enhances this process by extracting quantitative imaging features, revealing hidden patterns that can improve prognostic models and guide treatment decisions.

## Methods

This study utilized the UPENN-GBM (611 patients) and TCGA-GBM (135 patients) cohorts from TCIA [1][2]. UPENN-GBM served as the training set, while TCGA-GBM was for independent testing. Both automatic and manually corrected tumor segmentations were available. Voxel-wise radiomics features were extracted from T1-weighted-Gd and T2-weighted images using PyRadiomics v3.1.0 [3] (Figure 1). K-means clustering (k = 3) was arbitrary applied with background exclusion, and cluster volumes were calculated. Features with a Pearson correlation > 0.9 were removed, and the remaining features, along with patient age, were used as predictors in a Cox regression model

<figure><img src="image_3.png" /><figcaption>Figure 1: Voxel-wise radiomics features extraction.</figcaption></figure>

## Results

Habitat maps obtained after unsupervised clustering of spatial radiomics are shown in Figure 2. Thousands of models were built, trained, and tested using the remaining features after Pearson correlation analysis. The performance of the best models is shown in Table 1 and Kaplan-Meier survival curves (Figures 3 and 4) illustrating risk stratification results.

<figure><img src="image_4.png" /><figcaption>Figure 2: Habitat maps from the GLRLM High Gray Level Run Emphasis feature for patients with (a) good prognosis (survival = 6000 days) and (b) poor prognosis (survival = 100 days).</figcaption></figure>

<table><caption>Table 1: Performance comparison of radiomics models (with 4-fold cross-validation on the training set).</caption><thead><tr><th>Model</th><th>C-Index (Training)</th><th>95% CI (Training)</th><th>C-Index (Test)</th><th>95% CI (Test)</th><th>P-Value</th></tr></thead><tbody><tr><td>T1-weighted-GD</td><td>0.639</td><td>0.526 – 0.738</td><td>0.647</td><td>0.581 – 0.710</td><td>&lt; 0.05</td></tr><tr><td>T2-weighted</td><td>0.635</td><td>0.522 – 0.734</td><td>0.652</td><td>0.592 – 0.711</td><td>&lt; 0.05</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Figure 3: Kaplan-Meier curve based on a model integrating (age, and habitat 2 volume) extracted from gadolinium-enhanced T1-weighted images (Training set).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4: Kaplan-Meier curve of the model inferred on the test set.</figcaption></figure>

## Discussion and conclusion

The proposed approach proved effective for survival prediction and risk stratification. No significant difference in performance was observed between models based on gadolinium-enhanced T1- and T2-weighted images. To enhance reproducibility and generalizability, the model was designed with simplicity in mind, relying only on one radiomic feature and patient age. Despite its simplicity, habitat-based radiomics presents a promising avenue for survival prediction and risk stratification, highlighting the potential of regionalized radiomics in precision oncology.

## References:

[1] Bakas, Spyridon, et al. "The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics." Scientific data 9.1 (2022): 453. [2] Scarpace, Lisa, et al. "The cancer genome atlas glioblastoma multiforme collection (TCGA-GBM)." The Cancer Imaging Archive (2016). [3] Van Griethuysen, Joost JM, et al. "Computational radiomics system to decode the radiographic phenotype." Cancer research 77.21 (2017): e104-e107.

Walid.dandachly@creatis.insa-lyon.fr