零样本大模型自动规划放疗，效果媲美专家且更优控温。
Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planning
- 用大模型代理直接操控临床系统，零样本迭代优化放疗参数。
- 对20例头颈癌患者测试，器官保护相当，热点控制与适形度更优。
- 无需训练或调优，可推广至各类医院，适合放疗自动化需求者。
放射治疗计划制定是依赖经验的迭代过程，癌症病例增长使人工规划难以为继，亟需自动化。本研究提出一种基于大语言模型（LLM）代理的工作流程，用于调强放疗（IMRT）逆向计划。该代理直接与临床治疗计划系统（TPS）交互，迭代获取中间计划状态并提出新约束值以引导逆向优化。其决策结合当前观测与历史优化结果，实现策略动态调整。整个规划在零样本推理下完成，未接触过人工计划，也未进行微调或任务特定训练。在20例头颈癌患者上，将LLM生成计划与临床手动计划对比，分析关键剂量学指标。结果显示，LLM计划在器官危及组织（OAR）保护方面与临床计划相当，同时热点控制更优（最大剂量：106.5% vs. 108.8%），适形度显著提升（增强靶区CI：1.18 vs. 1.39；原发靶区CI：1.82 vs. 1.88）。本研究验证了零样本、基于大模型的自动化IMRT计划工作流在商用TPS中的可行性，提供了一种可泛化、临床可用的方案，有助于降低规划差异，推动人工智能放疗计划的广泛应用。
原文摘要 · Abstract (English)
Radiation therapy treatment planning is an iterative, expertise-dependent process, and the growing burden of cancer cases has made reliance on manual planning increasingly unsustainable, underscoring the need for automation. In this study, we propose a workflow that leverages a large language model (LLM)-based agent to navigate inverse treatment planning for intensity-modulated radiation therapy (IMRT). The LLM agent was implemented to directly interact with a clinical treatment planning system (TPS) to iteratively extract intermediate plan states and propose new constraint values to guide inverse optimization. The agent's decision-making process is informed by current observations and previous optimization attempts and evaluations, allowing for dynamic strategy refinement. The planning process was performed in a zero-shot inference setting, where the LLM operated without prior exposure to manually generated treatment plans and was utilized without any fine-tuning or task-specific training. The LLM-generated plans were evaluated on twenty head-and-neck cancer cases against clinical manual plans, with key dosimetric endpoints analyzed and reported. The LLM-generated plans achieved comparable organ-at-risk (OAR) sparing relative to clinical plans while demonstrating improved hot spot control (Dmax: 106.5% vs. 108.8%) and superior conformity (conformity index: 1.18 vs. 1.39 for boost PTV; 1.82 vs. 1.88 for primary PTV). This study demonstrates the feasibility of a zero-shot, LLM-driven workflow for automated IMRT treatment planning in a commercial TPS. The proposed approach provides a generalizable and clinically applicable solution that could reduce planning variability and support broader adoption of AI-based planning strategies.
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