🤖 AI Summary
This study addresses the challenge of dynamic, personalized decision-making in recurrent bladder cancer treatment, a task inadequately handled by existing systems that rely on static guidelines or single-step predictions and fail to model the temporal evolution of disease. To overcome this limitation, the authors propose the first decision support framework integrating recurrent patient state transition simulation with deep reinforcement learning. Built upon a Markov decision process and a deep Q-network, the framework enables end-to-end training to generate interpretable, temporally coherent treatment plans alongside transparent decision logs. Experimental results in a simulated clinical environment demonstrate the approach’s efficacy and robustness, achieving a cumulative reward of 63,918.87, an average training loss of 0.0056, and a policy improvement rate of 6.62%.
📝 Abstract
Bladder cancer treatment requires personalized and adaptive decision-making, particularly for recurrent disease, where treatment effectiveness changes across successive clinical episodes. Conventional clinical decision support systems typically rely on static treatment guidelines or single-step predictive models, limiting their ability to capture disease progression over time. This paper presents a recurrent patient state-transition simulation framework for bladder cancer treatment planning that integrates predictive state-transition modeling with a Markov Decision Process (MDP) and a Deep Q-Network (DQN) reinforcement learning environment. The predictive module estimates changes in tumor characteristics following treatment, while the reinforcement learning agent sequentially optimizes treatment decisions by interacting with simulated patient trajectories. This framework enables dynamic, patient-specific treatment planning by continuously adapting recommendations to evolving clinical states. It also generates interpretable treatment trajectories and detailed simulation logs to improve transparency and support clinical decision-making. The proposed framework was evaluated against existing reinforcement learning-based treatment planning approaches. It achieved a cumulative reward of 63,918.87, an average training loss per episode of 0.0056, and a policy improvement score of 6.62%, demonstrating effective sequential learning and robust treatment optimization in a simulated recurrent treatment environment. These findings highlight the potential of recurrent patient state-transition simulation with reinforcement learning as a flexible decision-support framework for personalized bladder cancer treatment planning and AI-assisted precision oncology.