Integrative Deep Learning from H&E Images Reveals Prognostically Distinct Pathology-Based Subtypes in Bladder Cancer
We present a pathology-based subtype of BCa that is portable, prognostically informative, and mechanistically concordant with RNA-defined biology. The approach offers a cost-effective and accessible alternative to traditional molecular profiling methods, with potential applications in personalized treatment and improved patient stratification.
Authors
- Xiaofu Qiu (ORCID: https://orcid.org/0000-0002-9246-6531)
- Miaoxiao Wang (ORCID: https://orcid.org/0000-0002-4636-0058)
- Feihan F. Dai (ORCID: https://orcid.org/0000-0001-6819-791X)
- Huanhui Li
- Zhenwei Wang (ORCID: https://orcid.org/0000-0001-7524-135X)
- Xiaoyang Li
- Zongtai Zheng (ORCID: https://orcid.org/0000-0001-7962-5166)
- Yongqiang Zhang (ORCID: https://orcid.org/0000-0002-1033-1132)
- Yanyan He
- Jia Fang
- Bi‐ling Zhong
Institutions
- Tongji University (CN)
- Sun Yat-sen University (CN)
- Jinan University (CN)
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- Shanghai Tenth People's Hospital (CN)
- Punan Hospital (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Current Cancer Drug Targets
- Published
- 2025-11-20
- DOI
- https://doi.org/10.2174/0115680096434051251113094536
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00