GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma.

ABSTRACT: Adult diffuse gliomas are clinically and molecularly heterogeneous, complicating risk stratification and personalized management. We introduce GlioSurv, a multimodal transformer model based on an accelerated failure time framework to integrate multiparametric MRI, clinical and molecular variables, and treatment data for personalized survival prediction. In a retrospective analysis of 1944 patients, including one internal cohort (n = 891; mean OS 32.2 months) and three external cohorts (n = 84, 470, 499; mean OS 26.1, 18.8, 19.0 months), GlioSurv demonstrated robust discrimination (IAUC: 0.68-0.86), calibration (IBS: 0.10-0.21) and concordance (C-index: 0.61-0.80). It significantly outperformed a convolutional neural network, a vision transformer, and a non-imaging multimodal transformer (p

SUBMITTER: Lee J 

PROVIDER: S-EPMC12618496 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature