Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer
This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.
Authors
- Xiaofu Qiu (ORCID: https://orcid.org/0000-0002-9246-6531)
- Feihan F. Dai (ORCID: https://orcid.org/0000-0001-6819-791X)
- Bangqi Wang (ORCID: https://orcid.org/0000-0002-2142-1969)
- Ji Liu (ORCID: https://orcid.org/0000-0002-8439-3600)
- Yongqiang Zhang (ORCID: https://orcid.org/0000-0002-6536-3393)
- Zhenwei Wang (ORCID: https://orcid.org/0000-0001-7524-135X)
- Zongtai Zheng (ORCID: https://orcid.org/0000-0001-7962-5166)
Institutions
- Tongji University (CN)
- Jinan University (CN)
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- Shanghai Tenth People's Hospital (CN)
- Guangdong Provincial People's Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1186/s12885-025-13688-x
- Citations
- 12
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 24.08
Funders
- National Natural Science Foundation of China