用大模型从病理报告中自动识别癌种、分期和预后，提升临床决策效率。
Cancer Type, Stage and Prognosis Assessment from Pathology Reports using LLMs
- 采用GPT、Mistral等大模型，零样本分析病理报告
- 新训练的Path-llama3.1-8B模型在三项任务中表现最优
- 适合医学信息提取与临床辅助诊断研究者使用
大型语言模型（LLMs）在自然语言处理任务中展现出显著潜力，但在病理学领域，特别是从非结构化医学文本（如病理报告）中提取有意义信息的应用仍处于探索阶段且缺乏量化评估。本研究利用最先进的语言模型，包括GPT系列、Mistral模型及开源Llama模型，评估其在全面分析病理报告中的表现，涵盖癌种识别、AJCC分期判断和预后评估，涉及信息抽取与高阶推理任务。基于零样本设置下的性能指标分析，我们开发了两个指令微调模型：Path-llama3.1-8B 和 Path-GPT-4o-mini-FT。这两项模型在零样本癌种识别、分期判定和预后评估任务中均优于其他对比模型。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown significant promise across various natural language processing tasks. However, their application in the field of pathology, particularly for extracting meaningful insights from unstructured medical texts such as pathology reports, remains underexplored and not well quantified. In this project, we leverage state-of-the-art language models, including the GPT family, Mistral models, and the open-source Llama models, to evaluate their performance in comprehensively analyzing pathology reports. Specifically, we assess their performance in cancer type identification, AJCC stage determination, and prognosis assessment, encompassing both information extraction and higher-order reasoning tasks. Based on a detailed analysis of their performance metrics in a zero-shot setting, we developed two instruction-tuned models: Path-llama3.1-8B and Path-GPT-4o-mini-FT. These models demonstrated superior performance in zero-shot cancer type identification, staging, and prognosis assessment compared to the other models evaluated.
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