Self‐Supervised <scp>DRL</scp> With Twin Critics: A Novel Framework for Glioblastoma Survival Prognostication

ABSTRACT The accurate survival prediction in glioblastoma patients remains a major challenge, largely owing to the considerable variability among brain tumor subtypes and their clinical profiles. This research introduces an innovative SimCLR‐Twin Critic DDPG framework that integrates self‐supervised representation learning with deep reinforcement learning for enhanced prognostic prediction. Tumor subregion segmentations were independently obtained using four advanced models namely nnU‐Net, VNet, SwinUNETR, and UNet. Based on the performance of these segmentation models, deep features were extracted from the nnU‐Net segmented tumor subregions and combined with handcrafted radiomics features. The combined features were subjected to the feature selection process using BorutaShap, followed by self‐supervised pretraining through SimCLR to improve feature representation and generalization. The optimized features were then fed into a Twin Critic DDPG agent designed for regression‐based survival time predictions. The proposed method outperformed existing techniques on the BraTS 2020 dataset, achieving the lowest MSE of 31 062.36 and the highest C‐index of 0.87 in overall survival prediction. To further confirm robustness and generalizability, the framework was externally validated on the BraTS 2019 dataset, where it achieved a comparable MSE of 31 156.56 and a C‐index of 0.86 using fused features. Additionally, LIME‐based local interpretability provided clinically relevant explanations for individual predictions, thereby enhancing trust in the AI‐driven system. This study highlights the SimCLR‐Twin Critic DDPG framework as a robust and interpretable solution for accurate survival prediction in glioblastoma patients.