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Dual-arc VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Lina Mekki¹, William Thomas Hrinivich², Junghoon Lee²

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.

Physics in Medicine and Biology

|October 27, 2025

Summary

This summary is machine-generated.

This study introduces a deep reinforcement learning (RL) framework for fast, automatic optimization of volumetric modulated arc therapy (VMAT) plans for prostate cancer. The AI achieved comparable plan quality to human experts, significantly reducing planning time.

Keywords:

VMAT deep reinforcement learning machine parameter optimization prostate cancer treatment planning

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Area of Science:

Medical Physics
Radiation Oncology
Artificial Intelligence in Healthcare

Background:

Volumetric Modulated Arc Therapy (VMAT) is a complex radiation therapy technique requiring precise machine parameter optimization.
Manual VMAT plan optimization is time-consuming and requires specialized expertise.
Deep Reinforcement Learning (RL) offers potential for automating and accelerating treatment planning processes.

Purpose of the Study:

To develop and evaluate a deep RL framework for rapid, automatic machine parameter optimization of VMAT plans for localized prostate cancer.
To assess the dosimetric quality and efficiency of the RL-generated plans compared to clinical standards.
To integrate the RL framework into a clinical treatment planning system (TPS) for seamless workflow adoption.

Main Methods:

A multi-task policy network combining convolution and long short-term memory was trained to predict machine parameters (dose rate, MLC positions).
The network utilized cumulative dose, patient anatomy (PTV, organs at risk), and historical machine parameters as input.
The framework was evaluated on 15 localized prostate cancer cases, comparing RL plans to existing clinical plans.

Main Results:

The RL framework generated deliverable dual-arc VMAT plans in an average of 20.7 ± 5.0 seconds.
No statistically significant dosimetric differences were found for mean rectum dose or bladder V6160 Gy compared to clinical plans.
RL plans required an additional 83.8 ± 7.2 seconds for automatic refinement to clinical quality within the TPS.

Conclusions:

The deep RL framework demonstrates potential for significantly streamlining VMAT treatment planning for prostate cancer.
The approach achieves comparable plan quality to human planners while drastically reducing optimization time.
This technology could enable more efficient adaptive radiation therapy through rapid plan generation and adaptation.