Deep learning-based survival prediction model for adult diffuse low-grade glioma: a multi-cohort validation study

Accurate prognostication in adult diffuse low-grade glioma (DLGG) patients remains challenging due to the complex interplay of clinical and molecular factors. This study aimed to develop and validate a deep learning-based model for predicting survival in DLGG patients. We analyzed 1,079 DLGG patients across three cohorts: training (n = 836), internal validation (n = 210), and external validation (n = 33). A deep learning model (DeepSurv) was developed incorporating seven clinicopathological variables. Model performance was assessed using C-index and integrated Brier scores (IBS). Feature importance was evaluated through permutation importance analysis and SHAP values. The cohorts demonstrated comparable baseline characteristics except for resection extent (P < 0.001). The model achieved robust performance with C-indices of 0.81, 0.76, and 0.87 in the training, internal validation, and external validation cohorts, respectively. Low IBS values (0.03–0.04) confirmed strong predictive accuracy across all cohorts. Age emerged as the strongest prognostic factor, showing non-linear effects particularly pronounced in IDH-wildtype tumors after age 50. IDH mutation status was the second most influential factor, while radiation therapy alone and tumor size showed limited prognostic value. Our deep learning model demonstrates reliable prognostic capabilities for DLGG patients, with age and IDH status as key determinants of survival. The model has been implemented as a web-based platform ( https://seerlggs-f4nze5jr7iuu9k9uuaemjf.streamlit.app ) for clinical use, offering personalized survival predictions. These findings contribute to more precise prognostication and may aid in treatment strategy optimization for DLGG patients.