Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study
This study demonstrates that LLMs, particularly when guided by the proposed IR strategy, can accurately and reliably perform esophageal cancer staging from free-text radiology reports. This approach not only provides high-performance predictions but also offers a transparent and verifiable reasoning process, highlighting its potential as a valuable decision-support tool to augment human expertise in complex clinical diagnostic tasks.
Authors
- Xingxing Cen (ORCID: https://orcid.org/0000-0002-1913-8590)
- Junyi Yuan (ORCID: https://orcid.org/0000-0002-3143-1343)
- Jiehui Jiang (ORCID: https://orcid.org/0000-0003-4948-3683)
- Min Wang (ORCID: https://orcid.org/0000-0002-3436-4246)
- Yao Yao (ORCID: https://orcid.org/0000-0002-4979-7204)
- Yinghui Xu (ORCID: https://orcid.org/0009-0002-7346-2794)
- Lu Gan
Institutions
- Shanghai University (CN)
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- Shanghai Chest Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-10-17
- DOI
- https://doi.org/10.2196/75556
- Citations
- 2
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.01