GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma.
Authors
Affiliations (12)
Affiliations (12)
- Interdisciplinary Programs in Cancer Biology, Seoul National University Graduate School, Seoul, Republic of Korea.
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Department of Electronic Engineering, Soongsil University, Seoul, Republic of Korea.
- Department of Intelligent Semiconductors, Soongsil University, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea. [email protected].
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea. [email protected].
- Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Korea. [email protected].
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 < 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.