Gamma Knife treatment planning using knowledge-based reinforcement learning
Christopher Huynh1, Björn Ahlgren2, Beibei Zhang1,3
1Medical Physics Department, Sunnybrook Health Sciences Centre, Toronto, Canada.
Medical Physics
|June 30, 2026
Summary
A new deep reinforcement learning agent was trained to optimize Gamma Knife radiosurgery plans by mimicking clinical decisions. This automation improves plan quality and consistency, reducing clinician workload.
Area of Science:
- Medical Physics
- Radiotherapy
- Machine Learning
Background:
- Inverse planning in Gamma Knife radiosurgery uses manually tuned weights to achieve clinical objectives.
- Manual tuning is case-specific, increasing clinical workload and potentially affecting plan quality consistency.
Purpose of the Study:
- To train a deep reinforcement learning (DRL) agent to automate inverse planning.
- The DRL agent uses a reward function incorporating clinical metrics from historical plans to guide optimization.
Main Methods:
- A neural network-based DRL agent was developed to adjust inverse planning weights.
- The agent inputs current plan metrics, dose distribution, and target/organ-at-risk masks.
- The approach was validated on single-target metastases and acoustic neuroma datasets.
Main Results:
- The DRL agent achieved significantly higher plan scores on the metastases test set (p=0.0136).
- The agent also showed improved plan scores on the acoustic neuroma test set (p=0.4493).
- Agent-generated plans demonstrated greater similarity to clinical plans across key quality metrics.
Conclusions:
- The DRL agent successfully learned to generate plans consistent with historical clinical decisions.
- Future research will explore incorporating additional inputs to further enhance agent performance by explaining planning variability.
