Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy

• URL: http://arxiv.org/abs/2506.09805v1
• Date: Wed, 11 Jun 2025 14:46:42 GMT
• Title: Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy
• Authors: Tonghe Wang, Yining Feng, Xiaofeng Yang,
• Abstract summary: In high-dose-rate ( HDR) prostate brachytherapy procedures, the pattern of needle placement solely relies on physician experience.<n>We investigated the feasibility of using reinforcement learning (RL) to provide needle positions and dwell times based on patient anatomy during pre-planning stage.<n>This approach would reduce procedure time and ensure consistent plan quality.
• Score: 3.198160082615183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: In high-dose-rate (HDR) prostate brachytherapy procedures, the pattern of needle placement solely relies on physician experience. We investigated the feasibility of using reinforcement learning (RL) to provide needle positions and dwell times based on patient anatomy during pre-planning stage. This approach would reduce procedure time and ensure consistent plan quality. Materials and Methods: We train a RL agent to adjust the position of one selected needle and all the dwell times on it to maximize a pre-defined reward function after observing the environment. After adjusting, the RL agent then moves on to the next needle, until all needles are adjusted. Multiple rounds are played by the agent until the maximum number of rounds is reached. Plan data from 11 prostate HDR boost patients (1 for training, and 10 for testing) treated in our clinic were included in this study. The dosimetric metrics and the number of used needles of RL plan were compared to those of the clinical results (ground truth). Results: On average, RL plans and clinical plans have very similar prostate coverage (Prostate V100) and Rectum D2cc (no statistical significance), while RL plans have less prostate hotspot (Prostate V150) and Urethra D20% plans with statistical significance. Moreover, RL plans use 2 less needles than clinical plan on average. Conclusion: We present the first study demonstrating the feasibility of using reinforcement learning to autonomously generate clinically practical HDR prostate brachytherapy plans. This RL-based method achieved equal or improved plan quality compared to conventional clinical approaches while requiring fewer needles. With minimal data requirements and strong generalizability, this approach has substantial potential to standardize brachytherapy planning, reduce clinical variability, and enhance patient outcomes.

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