Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer
This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.
Authors
- Anli Zhang (ORCID: https://orcid.org/0000-0003-4137-2225)
- Changsheng Ge
- Yi Shi (ORCID: https://orcid.org/0000-0002-1375-3621)
- Mingxin Huang (ORCID: https://orcid.org/0000-0002-8038-431X)
- Ao Li (ORCID: https://orcid.org/0000-0001-9910-8967)
- Minghui Wang (ORCID: https://orcid.org/0000-0002-5788-894X)
- Fang Zhao (ORCID: https://orcid.org/0009-0001-7942-533X)
- Wei Wang (ORCID: https://orcid.org/0000-0003-2211-8002)
- Zhenzhong Feng
- Haibo Wu
Institutions
- University of Science and Technology of China (CN)
- Anhui Medical University (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Diagnostic Pathology
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s13000-025-01707-1
- Citations
- 2
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- Anhui Medical University
- University of Science and Technology of China