This paper is in the following e-collection/theme issue:

Natural Language Processing (937) Clinical Information and Decision Making (2007) Decision Support for Health Professionals (1594) mHealth in a Clinical Setting (760) Innovations and Technology in Cancer Care (557) Machine Learning (2173) Cancer Prognosis Models and Machine Learning (72) Imaging Informatics (318)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75556, first published .
Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study

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

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

Authors of this article:

Yao Yao1 Author Orcid Image ;   Xingxing Cen1 Author Orcid Image ;   Lu Gan2 Author Orcid Image ;   Jiehui Jiang3 Author Orcid Image ;   Min Wang3 Author Orcid Image ;   Yinghui Xu4 Author Orcid Image ;   Junyi Yuan1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. May P, Greß J, Seidel C, Sommer S, Schuler M, Nokodian S, Schröder F, Jung J. Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data. JMIR Medical Informatics 2025;13:e78332 View
  2. Hu X, Feng L, Jing B, Luo L, Tan W, Li Y, Zheng X, Huang X, Lin S, Wu H, He L. Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation. Diagnostics 2026;16(2):215 View