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. 2025 Jul;173(3):505-514.
doi: 10.1007/s11060-025-05006-z. Epub 2025 Mar 18.

Survival prediction with radiomics for patients with IDH mutated lower-grade glioma

Alice Neimantaite  1 , Louise Carstam  2 , Tomás Gómez Vecchio  3   4 , Ida Häggström  5   6 , Tora Dunås  3 , Francesco Latini  7 , Maria Zetterling  7 , Malin Blomstrand  8   9 , Jiri Bartek  10   11   12 , Margret Jensdottir  10   11   12 , Erik Thurin  3   13 , Anja Smits  3 , Asgeir S Jakola  3   2
Affiliations

Survival prediction with radiomics for patients with IDH mutated lower-grade glioma

Alice Neimantaite et al. J Neurooncol. 2025 Jul.

Abstract

Purpose: Adult patients with diffuse lower-grade gliomas (dLGG) show heterogeneous survival outcomes, complicating postoperative treatment planning. Treating all patients early increases the risk of long-term side effects, while delayed treatment may lead to impaired survival. Refinement of prognostic models could optimize timing of treatment. Conventional radiological features are prognostic in dLGG, but MRI could carry more prognostic information. This study aimed to investigate MRI-based radiomics survival models and compare them with clinical models.

Methods: Two clinical survival models were created: a preoperative model (tumor volume) and a full clinical model (tumor volume, extent of resection, tumor subtype). Radiomics features were extracted from preoperative MRI. The dataset was divided into training set and unseen test set (70:30). Model performance was evaluated on test set with Uno's concordance index (c-index). Risk groups were created by the best performing model's predictions.

Results: 207 patients with mutated IDH (mIDH) dLGG were included. The preoperative clinical, full clinical and radiomics models showed c-indexes of 0.70, 0.71 and 0.75 respectively on test set for overall survival. The radiomics model included four features of tumor diameter and tumor heterogeneity. The combined full clinical and radiomics model showed best performance with c-index = 0.79. The survival difference between high- and low-risk patients according to the combined model was both statistically significant and clinically relevant.

Conclusion: Radiomics can capture quantitative prognostic information in patients with dLGG. Combined models show promise of synergetic effects and should be studied further in astrocytoma and oligodendroglioma patients separately for optimal modelling of individual risks.

Keywords: Glioma; Magnetic resonance imaging; Radiomics; Survival analysis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki. Regional Ethical Review Board in Gothenburg, Sweden (Dnr: 702 − 18, Dnr: 705 − 17) approved the study. Competing interests: The authors declare no competing interests. Consent to participate: Written informed consent was signed by the patients which were included prospectively. The ethical committee waived the need of informed consent for retrospectively included patients.

Figures

Fig. 1
Fig. 1
Visualization of 12 case examples of the radiomics tumor heterogeneity feature. (a1-3) visualizes patients with high values of the feature on T1c, (b1-3) patients with low values on T1c. (c1-3) visualizes patients with high values of the feature on FLAIR, (d1-3) patients with low values on FLAIR. Note that segmentations are done in FLAIR/T2-weighted images and may appear larger than the volume depicted in T1c images
Fig. 2
Fig. 2
Survival model interpretability. Feature importance and distribution on the test set by the (a) full clinical survival model, and (b) radiomics survival model. Positive SHAP values indicate increased risk whereas negative values indicate reduced risk
Fig. 3
Fig. 3
Kaplan-Meier survival curves of high- and low-risk patient groups within (a) entire patient cohort, (b) patients with astrocytoma, and (c) patients with oligodendroglioma
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