Actor Critic with Experience Replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy
Journal:
arXiv
Published Date:
Feb 1, 2025
Abstract
Background: Real-time treatment planning in IMRT is challenging due to
complex beam interactions. AI has improved automation, but existing models
require large, high-quality datasets and lack universal applicability. Deep
reinforcement learning (DRL) offers a promising alternative by mimicking human
trial-and-error planning.
Purpose: Develop a stochastic policy-based DRL agent for automatic treatment
planning with efficient training, broad applicability, and robustness against
adversarial attacks using Fast Gradient Sign Method (FGSM).
Methods: Using the Actor-Critic with Experience Replay (ACER) architecture,
the agent tunes treatment planning parameters (TPPs) in inverse planning.
Training is based on prostate cancer IMRT cases, using dose-volume histograms
(DVHs) as input. The model is trained on a single patient case, validated on
two independent cases, and tested on 300+ plans across three datasets. Plan
quality is assessed using ProKnow scores, and robustness is tested against
adversarial attacks.
Results: Despite training on a single case, the model generalizes well.
Before ACER-based planning, the mean plan score was 6.20$\pm$1.84; after,
93.09% of cases achieved a perfect score of 9, with a mean of 8.93$\pm$0.27.
The agent effectively prioritizes optimal TPP tuning and remains robust against
adversarial attacks.
Conclusions: The ACER-based DRL agent enables efficient, high-quality
treatment planning in prostate cancer IMRT, demonstrating strong
generalizability and robustness.
