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

Natural Language Processing (971) Clinical Information and Decision Making (2105) mHealth in a Clinical Setting (787) Decision Support for Health Professionals (1659) Innovations and Technology in Cancer Care (577) Imaging Informatics (341) Machine Learning (2261) Cancer Prognosis Models and Machine Learning (86)

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 (4) Tweetations Metrics

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  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
  3. Abikenari M. Artificial Intelligence Across the Surgical Oncology Continuum: Decision Support, Operative Intelligence, and a Translation-First Roadmap. Premier Journal of Science 2026 View
  4. Algarni A, Al-Eidan A, Khan S, Halwani M, Almusharraf A, Cho Y, Nam Y. Building novel LLM-enabled explainable ensemble transformer models combining endoscopic and CT images for discriminating the different grades of gastrointestinal cancers. Frontiers in Medicine 2026;13 View