Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.

ABSTRACT: We developed a named entity (NE) framework for information extraction from semi-structured clinical notes retrieved from The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) database and examined Large Language Models (LLMs) strategies to classify the 8^th edition of American Joint Committee on Cancer (AJCC) staging and American Thyroid Association (ATA) risk category for patients with well-differentiated thyroid cancer. The NE framework consisted of annotation guidelines development, ground truth labelling, prompting approaches, and evaluation codes. Four LLMs (Mistral-7B-Instruct, Llama-3.1-8B-Instruct, Gemma-2-9B-Instruct, and Qwen2.5-7B-Instruct) were offline utilised for information extraction, comparing with expert-curated ground truth. Our framework was developed using 50 TC

SUBMITTER: Fung MMH 

PROVIDER: S-EPMC11873034 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature