自动瘤国际疾病分类编码现实世界病理学报告使用自主托管的大型语言模型
Kamyar Arzideh1,2, René Hosch2,3, Amin Turki4,5,6
1Central IT Department, Data Integration Center, University Hospital Essen, Essen, Germany.
JCO clinical cancer informatics
|March 11, 2026
概括
大型语言模型 (LLM) 显示出在病理学报告中协助国际瘤疾病分类 (ICD-O) -3编码的前景,但尚未准备好用于无监督的临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 计算病理学计算病理学
背景情况:
- 使用国际瘤疾病分类 (ICD-O) -3进行病理学报告的手动编码是低效的,容易出现错误.
- 需要自动化解决方案来提高癌症编码的速度和准确性.
研究的目的:
- 评估最先进的大型语言模型 (LLM) 在提取ICD-O-3地形和形态代码方面的性能.
- 评估LLM在病理学编码中临床实施的潜力.
主要方法:
- 分析了来自10,823名患者的21,364份病理学报告.
- 在一个安全的医院IT基础设施上对五个LLM (Llama-3.3-70B-Instruct,DeepSeek-R1-Distill-Llama,Qwen3-235B-A22B,Gemma-3-12B-it) 的评估.
- 开发了用于拓和形态提取的三个提示符,通过精确和三位置代码匹配来测量性能.
主要成果:
- Qwen3-235B-A22B实现了最高精确的地形代码预测 (F1: 71.6%).
- 深度搜索-R1-蒸-拉玛-70B在形态代码预测方面领先 (精确F1: 34.7%).
- 模型在罕见条件下扎,表明概括不佳.
结论:
- 在专家指导的病理编码中,LLM显示出作为辅助工具的潜力.
- 目前的LLM性能不足以完全自动化,无监督的临床使用.
- 与地形学相比,LLM表现出对上下文的依赖性和对形态学的较低准确性.
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