A tandem reinforcement learning framework for localized prostate cancer treatment planning and machine parameter optimization

We successfully developed and validated a deep RL framework for VMAT MPO. The algorithm rapidly generates VMAT prostate cancer treatment plans that meet clinical constraints and are dosimetrically comparable to manually optimized plans without the use of a commercial TPS optimizer. This work demonstrates the feasibility of RL as a tool to fully automate the VMAT planning process, offering the potential to decrease planning times while maintaining plan quality.