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从病理学报告中提取肺癌的临床关键指标和生存预测,使用大型语言模型

Yung-Chun Chang¹, Shih-Hsin Hsiao², Wen-Chao Yeh³

¹Graduate Institute of Data Science, Taipei Medical University, New Taipei City, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City, Taiwan.

Computers in biology and medicine

|June 24, 2025

概括

预训练的语言模型 (PLM) 可以自动从肺癌病理报告中提取关键特征. 这提高了晚期肺癌患者的生存预测准确度,增强了临床决策.

科学领域:

在瘤学瘤学.
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

肺癌是癌症死亡的主要原因,通常是晚期诊断出来的.
病理学报告包含复杂的数据,阻碍了临床决策.
从报告中自动提取信息对于及时的护理至关重要.

研究的目的:

评估预先训练的语言模型 (PLMs) 来从肺癌病理报告中提取NCCN定义的特征.
评估提取特征对预测晚期肺癌生存率的有用性.
为了验证不同机构的模型性能.

主要方法:

分析了来自4600名晚期肺癌患者的2万份病理报告.
利用微调的LLaMA 3进行特征提取和生存预测.
雇员10倍交叉验证和外部跨医院验证.

主要成果:

精心调整的LLaMA 3在特征提取方面获得了92%的F1得分.
在晚期肺癌中获得了70%的F1得分,用于预测晚期肺癌的生存率.
在医院中表现出强大而普遍的性能.

结论:

PLM,特别是LLaMA 3,显示出自动化临床数据提取的巨大潜力.
自动提取可以提高肺癌生存预测的准确性.
这项技术可以改善临床决策和患者的治疗结果.

关键词:

临床特征提取临床特征提取肺癌的病理学肺癌的病理学自然语言处理自然语言处理.预先训练有素的语言模型预测生存的预测.

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