MARTP: a multi-agent simulation framework for automated radiation therapy planning based on LLMs

OBJECTIVE: High-quality radiotherapy requires accurate dose delivery to target volumes while protecting organs-at-risk (OARs). However, current clinical workflows remain constrained by labor-intensive multidisciplinary collaboration, prolonged planning cycles, and limited scalability. Intelligent automation capable of integrating clinical knowledge and real-world decision patterns is needed to enhance precision and efficiency in radiotherapy planning. This study proposes MARTP, a Multi-Agent Radiation Therapy Planning framework driven by large language models (LLMs), to emulate multidisciplinary clinical workflows and enable end-to-end intelligent radiotherapy planning and evaluation. Approach: MARTP coordinates five specialized agents to integrate data analysis, weight adjustment, plan optimization, plan evaluation, and report generation into a unified radiotherapy planning workflow. The framework leverages supervised fine-tuning (SFT) on expert weight adjustment demonstrations to improve adaptation to complex clinical cases, incorporates retrieval-augmented generation (RAG) to ground planning decisions in case-specific knowledge, and employs a predefined model-context protocol (MCP) to enable high-precision treatment plan generation. In addition, reinforcement learning (RL) with expert preference data is used to develop an intelligent plan evaluation mechanism. Results: Statistical analysis reveals that the dosimetric metrics of plans generated by MARTP exhibit no statistically significant differences compared with expert-crafted clinical plans, while the planning efficiency is substantially improved. In addition, the framework demonstrates robust and safe behavior under abnormal input scenarios, maintaining clinically acceptable outputs. The SFT and RL components further enhance the consistency, semantic accuracy, and reliability of model-generated weight adjustments and plan evaluations. Significance: MARTP demonstrates that LLMs-driven multi-agent systems can effectively replicate multidisciplinary radiotherapy workflows, generating clinically comparable plans with substantially improved efficiency. The framework provides a promising pathway for integrating intelligent automation into radiotherapy practice, supporting more consistent decision-making and scalable treatment planning.