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

Le résumé fourni par la source

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 < 0.01). Sequential integration of imaging, clinical, molecular, then treatment data, progressively improved C-index from 0.69 to 0.80 (p < 0.001). Interpretability analyses confirmed established prognostic factors and indicate the potential of GlioSurv to support personalized survival prediction and risk-stratified decision-making in diffuse glioma.

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