Large Language Model-Based Automated Tumor, Node, Metastasis Staging and Resectability Assessment for Pancreatic Cancer in Radiology Reports With Detection of Incomplete Documentation

Abstract

Claude 3.7 Sonnet demonstrated high accuracy in extracting structured pancreatic cancer staging information from unstructured Japanese radiology reports without task-specific training. While challenges remain in interpreting nuanced descriptions of vascular invasion and resectability, the model reliably identified most staging elements and omissions. These findings highlight the potential of LLMs as tools for semi-automated generation of structured data from routine free-text reports, which could improve reporting consistency, workflow efficiency, and secondary data utilization in oncology care.

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