Survival prediction with radiomics for patients with IDH mutated lower-grade glioma
Journal article, 2025

PurposeAdult 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.MethodsTwo 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.Results207 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.ConclusionRadiomics 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.

Magnetic resonance imaging

Radiomics

Glioma

Survival analysis

Author

Alice Neimantaite

University of Gothenburg

Louise Carstam

Sahlgrenska University Hospital

Tomas Gomez Vecchio

University of Gothenburg

Ida Häggström

University of Gothenburg

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Tora Dunas

University of Gothenburg

Francesco Latini

Akademiska Sjukhuset

Maria Zetterling

Akademiska Sjukhuset

Malin Blomstrand

University of Gothenburg

Jiri Bartek

Karolinska University Hospital

Rigshospitalet

Karolinska Institutet

Margret Jensdottir

Karolinska University Hospital

Karolinska Institutet

Rigshospitalet

Erik Thurin

University of Gothenburg

Anja Smits

University of Gothenburg

Asgeir S. Jakola

University of Gothenburg

Journal of Neuro-Oncology

0167-594X (ISSN) 1573-7373 (eISSN)

Vol. In Press

Subject Categories (SSIF 2025)

Cancer and Oncology

Neurology

DOI

10.1007/s11060-025-05006-z

PubMed

40100522

More information

Latest update

3/28/2025