Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers
Here, we leveraged automatic tile-level annotations from H&E slides to efficiently develop deep learning-based IHC biomarker models, achieving AUCs between 0.90 and 0.96. AI generated IHC showed substantial concordance with conventional IHC across most markers, supporting its potential as an assistive tool in routine diagnostics.
Authors
- Lianlian Wu (ORCID: https://orcid.org/0000-0002-9611-4488)
- Zehua Dong (ORCID: https://orcid.org/0000-0002-2652-0923)
- Wenzhao Liu (ORCID: https://orcid.org/0000-0001-7506-0663)
- Jia Li (ORCID: https://orcid.org/0000-0002-4399-6528)
- Fang-Fang Chen (ORCID: https://orcid.org/0000-0002-6944-9522)
- Honggang Yu (ORCID: https://orcid.org/0000-0001-5986-1284)
- Mei Deng
- Junxiao Wang (ORCID: https://orcid.org/0000-0003-2638-0630)
- Shiying Zhang (ORCID: https://orcid.org/0000-0003-0984-0260)
- Zhi Zeng
Institutions
- Wuhan University (CN)
- Renmin Hospital of Wuhan University (CN)
Publication Details
- Journal
- BMC Gastroenterology
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1186/s12876-025-04045-0
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Natural Science Foundation of China
- Wuhan University
- National Key Research and Development Program of China
- Renmin Hospital of Wuhan University