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Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep

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  • 1Department of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, USA.

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This study introduces an automated framework using reinforcement learning (RL) for high-dose-rate (HDR) brachytherapy planning in cervical cancer, improving plan quality and efficiency over manual methods.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence in Medicine

Background:

  • High-dose-rate (HDR) brachytherapy is crucial for locally advanced cervical cancer treatment.
  • Current HDR brachytherapy planning relies heavily on manual expertise, leading to variability and inefficiency.
  • Automating treatment planning can enhance consistency and efficiency in HDR brachytherapy.

Purpose of the Study:

  • To develop a fully automated HDR brachytherapy planning framework.
  • To integrate reinforcement learning (RL) and dose-based optimization for improved treatment plans.
  • To achieve clinically acceptable plans with enhanced consistency and efficiency.

Main Methods:

  • A hierarchical two-stage autoplanning framework combining deep Q-network (DQN)-based RL and Adam-based optimization.
  • RL agent iteratively selects treatment planning parameters (TPPs) balancing target coverage and organ-at-risk (OAR) sparing.
  • Dose-volume histogram (DVH) metrics and clinical dose objectives guide the RL agent and optimizer.

Main Results:

  • The automated framework successfully learned clinically meaningful TPP adjustments across diverse anatomies.
  • RL-based automated planning achieved a higher average score (93.89%) compared to manual clinical plans (91.86%) for unseen patients.
  • Improvements were maintained with full target coverage and reduced CTV hot spots in most cases.

Conclusions:

  • The proposed automated HDR brachytherapy planning framework demonstrates superior performance over manual planning.
  • This AI-driven approach offers a promising solution for consistent and efficient treatment planning in cervical cancer.
  • Further validation can establish this framework as a standard in clinical practice.