Survival prediction with radiomics for patients with IDH mutated lower-grade glioma
Artikel i vetenskaplig tidskrift, 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

Författare

Alice Neimantaite

Göteborgs universitet

Louise Carstam

Sahlgrenska universitetssjukhuset

Tomas Gomez Vecchio

Göteborgs universitet

Ida Häggström

Göteborgs universitet

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Tora Dunas

Göteborgs universitet

Francesco Latini

Akademiska Sjukhuset

Maria Zetterling

Akademiska Sjukhuset

Malin Blomstrand

Göteborgs universitet

Jiri Bartek

Karolinska universitetssjukhuset

Rigshospitalet

Karolinska Institutet

Margret Jensdottir

Karolinska universitetssjukhuset

Karolinska Institutet

Rigshospitalet

Erik Thurin

Göteborgs universitet

Anja Smits

Göteborgs universitet

Asgeir S. Jakola

Göteborgs universitet

Journal of Neuro-Oncology

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

Vol. In Press

Ämneskategorier (SSIF 2025)

Cancer och onkologi

Neurologi

DOI

10.1007/s11060-025-05006-z

PubMed

40100522

Mer information

Senast uppdaterat

2025-03-28