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Related Experiment Video

Updated: May 21, 2026

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
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Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System.

Jinsheng Li1,2, Peng Guo2, Xiangyu Guo3

  • 1Radiation Oncology, University of Miami Miller School of Medicine, Miami, USA.

Cureus
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

Automated treatment planning for rotating gamma systems (RGS) using deep reinforcement learning (DRL) significantly reduces planning time and reliance on expertise. This DRL algorithm achieves comparable plan quality to manual methods for brain tumor stereotactic radiosurgery (SRS).

Keywords:
automated planningdeep reinforcement learninggamma knife stereotactic radiosurgeryrotational gamma systemsrs

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Traditional treatment planning for rotating gamma systems (RGS) is complex, time-consuming, and heavily dependent on planner experience.
  • Manual optimization of shot parameters (size, location, weight) involves extensive trial and error to achieve target coverage and dose conformity.
  • This process can lead to variability and inefficiency in stereotactic radiosurgery (SRS) planning.

Purpose of the Study:

  • To develop and evaluate an automated treatment planning algorithm for RGS using deep reinforcement learning (DRL).
  • To assess the feasibility and effectiveness of DRL in generating clinically acceptable RGS treatment plans.
  • To compare the quality and efficiency of DRL-generated plans against traditional, planner-driven methods.

Main Methods:

  • A deep reinforcement learning (DRL) algorithm, specifically proximal policy optimization (PPO), was developed for RGS treatment planning.
  • Shot placement was modeled as a sequential decision-making process, incorporating target geometry and cumulative dose distribution.
  • The algorithm was trained and validated on 45 clinical brain tumor cases, using target dose coverage (Cov) and conformal index as reward parameters.

Main Results:

  • The DRL algorithm successfully generated clinically acceptable plans for brain tumors across various sizes (0.5-45 cc).
  • Achieved average dose coverage (Cov) and selectivity metrics comparable to experienced planners, with plan generation time under 10 minutes.
  • Independent validation demonstrated high Cov (96%) and selectivity (0.81), with no statistically significant difference in key planning metrics (Cov, selectivity, CI) compared to manual plans.

Conclusions:

  • The DRL-based automated planning algorithm is feasible for RGS treatment planning.
  • This approach offers comparable plan quality to manual methods while substantially reducing planning time and user dependency.
  • The DRL algorithm shows significant potential to enhance efficiency and consistency in stereotactic radiosurgery (SRS) planning.