Cancer stage classification is important for making treatment and care
management plans for oncology patients. Information on staging is often
included in unstructured form in clinical, pathology, radiology and other
free-text reports in the electronic health record system, requiring extensive
work to parse and obtain. To facilitate the extraction of this information,
previous NLP approaches rely on labeled training datasets, which are
labor-intensive to prepare. In this study, we demonstrate that without any
labeled training data, open-source clinical large language models (LLMs) can
extract pathologic tumor-node-metastasis (pTNM) staging information from
real-world pathology reports. Our experiments compare LLMs and a BERT-based
model fine-tuned using the labeled data. Our findings suggest that while LLMs
still exhibit subpar performance in Tumor (T) classification, with the
appropriate adoption of prompting strategies, they can achieve comparable
performance on Metastasis (M) classification and improved performance on Node
(N) classification.

通过使用开源临床大型语言模型（LLMs）和基于 BERT 的模型进行细化调整，本研究证明在没有标记训练数据的情况下，可以从真实世界的病理报告中提取病理肿瘤 - 淋巴结 - 转移（pTNM）分期信息。研究发现 LLMs 在肿瘤（T）分类方面仍然表现不佳，但通过适当的提示策略，它们可以达到与转移（M）分类相当的性能，并在淋巴结（N）分类方面表现出更好的性能。