用大模型自动调优放疗计划权重，提升精准度与效率。
An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimization
- 大模型通过迭代反馈动态调整放疗权重
- 23例患者验证，靶区剂量均匀性与器官保护优于固定权重方案
- 结合检索增强生成，实现临床知识驱动的智能决策
种子植入近距离放疗（SIBT）是有效的癌症治疗手段，但临床计划常依赖人工调整目标函数权重，效率低且结果欠佳。本研究提出一种基于大语言模型（LLM）的自适应权重优化框架，用于SIBT计划自动化。采用本地部署的DeepSeek-R1 LLM，与自动计划算法形成迭代循环：初始使用固定权重生成计划，随后由LLM评估计划质量并推荐新权重，直至满足收敛条件；最终由LLM进行综合评估以确定最优方案。构建并利用检索增强生成（RAG）技术的临床知识库，支持模型开展领域特定推理。在23例患者病例上验证表明，该方法生成的计划在临床靶区（CTV）剂量均一性及危及器官（OARs）保护方面，达到或超过临床认可的固定权重计划水平，展示了大模型在SIBT规划自动化中的应用潜力。
原文摘要 · Abstract (English)
Seed implant brachytherapy (SIBT) is an effective cancer treatment modality; however, clinical planning often relies on manual adjustment of objective function weights, leading to inefficiencies and suboptimal results. This study proposes an adaptive weight optimization framework for SIBT planning, driven by large language models (LLMs). A locally deployed DeepSeek-R1 LLM is integrated with an automatic planning algorithm in an iterative loop. Starting with fixed weights, the LLM evaluates plan quality and recommends new weights in the next iteration. This process continues until convergence criteria are met, after which the LLM conducts a comprehensive evaluation to identify the optimal plan. A clinical knowledge base, constructed and queried via retrieval-augmented generation (RAG), enhances the model's domain-specific reasoning. The proposed method was validated on 23 patient cases, showing that the LLM-assisted approach produces plans that are comparable to or exceeding clinically approved and fixed-weight plans, in terms of dose homogeneity for the clinical target volume (CTV) and sparing of organs at risk (OARs). The study demonstrates the potential use of LLMs in SIBT planning automation.
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