Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
High-dose-rate (HDR) brachytherapy plays a critical role in the treatment of
locally advanced cervical cancer but remains highly dependent on manual
treatment planning expertise. The objective of this study is to develop a fully
automated HDR brachytherapy planning framework that integrates reinforcement
learning (RL) and dose-based optimization to generate clinically acceptable
treatment plans with improved consistency and efficiency. We propose a
hierarchical two-stage autoplanning framework. In the first stage, a deep
Q-network (DQN)-based RL agent iteratively selects treatment planning
parameters (TPPs), which control the trade-offs between target coverage and
organ-at-risk (OAR) sparing. The agent's state representation includes both
dose-volume histogram (DVH) metrics and current TPP values, while its reward
function incorporates clinical dose objectives and safety constraints,
including D90, V150, V200 for targets, and D2cc for all relevant OARs (bladder,
rectum, sigmoid, small bowel, and large bowel). In the second stage, a
customized Adam-based optimizer computes the corresponding dwell time
distribution for the selected TPPs using a clinically informed loss function.
The framework was evaluated on a cohort of patients with complex applicator
geometries. The proposed framework successfully learned clinically meaningful
TPP adjustments across diverse patient anatomies. For the unseen test patients,
the RL-based automated planning method achieved an average score of 93.89%,
outperforming the clinical plans which averaged 91.86%. These findings are
notable given that score improvements were achieved while maintaining full
target coverage and reducing CTV hot spots in most cases.
