通过使用开源大型语言模型协调面临风险的器官结构名称
Adrian Thummerer1, Matteo Maspero2, Erik van der Bijl3
1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Physics and imaging in radiation oncology
|August 12, 2025
概括
开源大型语言模型 (LLM) 准确地使用标准化命名法更名为风险器官 (OAR) 结构. 推理增强的LLM表现出卓越的性能,促进了辐射瘤学的AI发展.
科学领域:
- 辐射瘤学 辐射瘤学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 放射治疗结构的标准化命名对于自动化,协作和AI研究至关重要.
- 目前实施的命名准则 (AAPM-TG-263) 面临着挑战.
- 对于多机构和多语言数据集,需要自动化OAR重命名.
研究的目的:
- 评估开源大型语言模型 (LLM) 用于自动化风险器官 (OAR) 重命名.
- 在多样化,多机构和多语言数据集上评估LLM绩效.
- 为了比较推理与非推理的LLM和不确定性估计方法.
主要方法:
- 四个开源的LLM (Llama 3.3,Llama 3.3 R1,DeepSeek V3,DeepSeek R1) 进行了测试.
- 使用了来自1684名患者的34,177个OAR结构的数据集.
- 采用了近射线提示和蒙特卡洛抽样以确定不确定性.
主要成果:
- 实现了高的重命名精度,DeepSeek R1达到98.6%的独特精度.
- 推理增强的LLM优于非推理模型.
- 蒙特卡洛采样显示出比基于提示的信心更好的错误相关性和检测.
结论:
- 开源的LLM可以在不同的数据集中准确地协调OAR命名法.
- 法律法规促进了TG-263的采用和研究标准化数据集的创建.
- 推理LLM对辐射瘤学的AI发展有希望.
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