在肿瘤学中，电子健康记录包含用于癌症患者的诊断、分期和治疗计划的文本关键信息。然而，文本数据处理需要大量的时间和精力，这限制了这些数据的利用。自然语言处理（NLP）技术的最新进展，包括大型语言模型，可以应用于癌症研究。特别是，从手术病理报告中提取病理分期所需的信息可用于根据最新的癌症分期指南更新癌症分期。本研究有两个主要目标。第一个目标是评估从基于文本的手术病理报告中提取信息以及使用微调的生成语言模型（GLM）根据提取的信息确定肺癌患者的病理阶段的性能。第二个目标是确定在资源有限的计算环境中利用相对较小的 GLM 进行信息提取的可行性。肺癌手术病理报告收集自三级医院盆唐首尔国立大学医院 (SNUBH) 的通用数据模型数据库在韩国。我们根据这些报告选择了肿瘤淋巴结 (TN) 分类所需的 42 个描述符，并创建了经过两位临床专家验证的黄金标准。病理报告和黄金标准用于生成用于训练和评估 GLM 的提示响应对，然后使用 GLM 从病理报告中提取分期所需的信息。我们评估了六个经过训练的模型的信息提取性能以及它们在 TN 中的性能使用提取的信息进行分类。使用演绎数据集进行预训练的 Deduction Mistral-7B 模型总体表现最佳，在信息提取问题中的精确匹配率为 92.24%，在信息提取问题中的准确度为 0.9876（同时预测 T 和 N 分类）。这项研究表明，使用演绎数据集训练 GLM 可以提高信息提取性能，并且参数数量相对较少（大约 70 亿）的 GLM 可以在此问题上实现高性能。所提出的基于 GLM 的信息提取方法预计可用于临床决策支持、肺癌分期和研究。版权所有 © 2024 作者。由爱思唯尔公司出版。保留所有权利。

In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines.This study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment.Lung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports.We evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification.This study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.