用大模型自动优化放疗方案，全程本地运行不泄露隐私。
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent
- 用大模型+检索增强+强化学习，在本地完成放疗计划自动优化。
- 700亿参数模型比80亿的最终评分高16.4%，检索增强提升19.8%。
- 支持可解释的自然语言推理，适合临床放疗自动化部署。
放疗计划制定过程复杂且耗时，常受规划者间差异和主观判断影响。为解决此问题，我们提出剂量优化语言代理（DOLA），一种基于大语言模型（LLaMa3.1）的自主代理，可在严格保护患者隐私的前提下优化放疗计划。DOLA直接集成于商用治疗计划系统，采用思维链提示、检索增强生成（RAG）与强化学习（RL）。所有操作均在安全本地环境中进行，无需外部数据共享。我们在18例前列腺癌患者（60 Gy/20次）的回顾性队列上评估，对比了80亿与700亿参数模型及三种策略（No-RAG、RAG、RAG+RL）在10轮规划中的表现。700亿模型最终得分较80亿模型高出约16.4%；使用RAG相比无RAG基线提升19.8%；引入RL加速收敛，体现检索记忆与强化学习的协同效应。最优温度超参分析显示0.4在探索与利用间取得最佳平衡。本研究首次成功实现本地部署的大模型代理在商用放疗系统中自主优化计划，通过可解释的自然语言推理扩展人机交互，提供可扩展且隐私友好的框架，具有显著临床应用潜力。
原文摘要 · Abstract (English)
Radiotherapy treatment planning is a complex and time-intensive process, often impacted by inter-planner variability and subjective decision-making. To address these challenges, we introduce Dose Optimization Language Agent (DOLA), an autonomous large language model (LLM)-based agent designed for optimizing radiotherapy treatment plans while rigorously protecting patient privacy. DOLA integrates the LLaMa3.1 LLM directly with a commercial treatment planning system, utilizing chain-of-thought prompting, retrieval-augmented generation (RAG), and reinforcement learning (RL). Operating entirely within secure local infrastructure, this agent eliminates external data sharing. We evaluated DOLA using a retrospective cohort of 18 prostate cancer patients prescribed 60 Gy in 20 fractions, comparing model sizes (8 billion vs. 70 billion parameters) and optimization strategies (No-RAG, RAG, and RAG+RL) over 10 planning iterations. The 70B model demonstrated significantly improved performance, achieving approximately 16.4% higher final scores than the 8B model. The RAG approach outperformed the No-RAG baseline by 19.8%, and incorporating RL accelerated convergence, highlighting the synergy of retrieval-based memory and reinforcement learning. Optimal temperature hyperparameter analysis identified 0.4 as providing the best balance between exploration and exploitation. This proof of concept study represents the first successful deployment of locally hosted LLM agents for autonomous optimization of treatment plans within a commercial radiotherapy planning system. By extending human-machine interaction through interpretable natural language reasoning, DOLA offers a scalable and privacy-conscious framework, with significant potential for clinical implementation and workflow improvement.
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