Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.
Journal:
Medical physics
Published Date:
May 31, 2025
Abstract
BACKGROUND: Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions between radiation beams and the human body. The introduction of artificial intelligence (AI) has automated treatment planning, significantly improving efficiency. However, existing automatic treatment planning agents often rely on supervised or unsupervised AI models that require large datasets of high-quality patient data for training. Additionally, these networks are generally not universally applicable across patient cases from different institutions and can be vulnerable to adversarial attacks. Deep reinforcement learning (DRL), which mimics the trial-and-error process used by human planners, offers a promising new approach to address these challenges. PURPOSE: This work aims to develop a stochastic policy-based DRL agent for automatic treatment planning that facilitates effective training with limited datasets, universal applicability across diverse patient datasets, and robust performance under adversarial attacks.
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