An Artificial Intelligence-Based Method for Risk Stratification of Urothelial Carcinoma from Liquid-Based Urine Cytology Whole-Slide Images
The proposed AI-assisted framework shows robust and interpretable performance in stratifying UC cytological categories from WSIs. It holds strong potential as a supportive tool in urine cytology, especially in assisting with the diagnosis of high-risk UC cases.
Authors
- Zichang Liu (ORCID: https://orcid.org/0000-0003-2805-7925)
- Jia Li (ORCID: https://orcid.org/0000-0002-4969-7158)
- Xinyi Cao (ORCID: https://orcid.org/0000-0002-3476-9833)
- Fengqi Fang
- Jianbo Wang (ORCID: https://orcid.org/0000-0002-3315-3105)
- Ying Li (ORCID: https://orcid.org/0000-0001-5062-0230)
- Lei Xiong (ORCID: https://orcid.org/0009-0004-3570-9332)
- Xinyi Jin (ORCID: https://orcid.org/0000-0002-2222-0958)
- Chang Shi
- Chen Pan (ORCID: https://orcid.org/0000-0002-1277-4463)
- X Zhang (ORCID: https://orcid.org/0000-0002-4686-2840)
- Lizhi Zhang (ORCID: https://orcid.org/0009-0009-3547-754X)
Institutions
- Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- Acta Cytologica
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1159/000548615
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00