Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study.

ABSTRACT:

Background

Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced staging accuracy. Recent advances in large language models (LLMs) have shown promise in medical applications, but their utility in esophageal cancer staging remains underexplored.

Objective

This study aims to compare the performance of 3 locally deployed LLMs (INF-72B, Qwen2.5-72B, and LLaMA3.1-70B) and clinicians in preoperative esophageal cancer staging using free-text radiology reports.

Methods

This retrospective study included 200 patients from Shanghai Chest Hospital who underwent esophageal cancer surgery from May to D

SUBMITTER: Yao Y 

PROVIDER: S-EPMC12533932 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature