Logo image
Back
Classifying Cancer Stage with Open-Source Clinical Large Language Models
Preprint   Open access

Classifying Cancer Stage with Open-Source Clinical Large Language Models

Chia-Hsuan Chang, Mary M Lucas, Grace Lu-Yao and Christopher C Yang
ArXiv.org
01 Apr 2024
DOI: https://doi.org/10.48550/arxiv.2404.01589
url
https://doi.org/10.48550/arxiv.2404.01589View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Artificial Intelligence Computer Science - Computation and Language
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.

Metrics

14 Record Views

Details

Logo image