从现实世界瘤学报告中使用大型语言模型进行临床推理
Jun Hyeong Park1,2, Seonhwa Kim1, Jaesung Heo1
1Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea.
Digital health
|November 17, 2025
概括
大型语言模型 (LLM) 在提取瘤学数据和从放射学和病理学报告中执行临床推理方面表现出强的表现. 结构化的提示提升了LLM的能力,用于诸如瘤分期和响应分类等任务.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 放射学和病理学报告包含了癌症诊断和治疗的关键信息.
- 从这些报告中提取结构化数据并从中进行临床推断是复杂且耗时的.
- 大型语言模型 (LLM) 显示了自动化这些任务的潜力.
研究的目的:
- 评估LLM从瘤学报告中进行结构化信息提取和基于指南的临床推断的能力.
- 评估Gemma LLM的表现,有或没有结构化的推理提示.
- 建立一个基准数据集来评估LLM在瘤学报告分析方面的表现.
主要方法:
- 从3650个放射学和588个病理学报告中创建了一个问答 (Q&A) 基准数据集.
- 任务包括直接提取基因组/组织学发现和临床推理 (RECIST,AJCC TNM分期).
- 将Gemma 4B和Gemma 12B模型与基于临床指南的结构化提示进行了比较.
主要成果:
- 杰玛12B模型在直接从病理学报告中提取高F1分数 (92.6-93.3) 取得了成功.
- 通过结构化提示,Gemma 12B在推理任务中显著改善:瘤反应 (81.5),T阶段 (74.3),N阶段 (87.1) 和M阶段 (90.8).
- 杰玛4B模型表现出不一致的性能,有时会因推理提示而退化.
结论:
- LLM可以在现实世界瘤放射学报告上执行基于指导方针的复杂临床推理.
- 将临床标准 (RECIST/AJCC) 与结构化提示结合起来,使LLM能够支持细微的推断.
- 这表明了LLM在瘤学数据分析中的未来临床应用的潜力.
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