A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study
We successfully constructed a stacking ensemble machine learning model for early, non-invasive identification of histological variants in bladder cancer which will help urologists make clinical decisions.
Authors
- Qing Jiang (ORCID: https://orcid.org/0000-0003-1875-6263)
- Fajin Lv (ORCID: https://orcid.org/0000-0002-5746-168X)
- Mingzhao Xiao (ORCID: https://orcid.org/0000-0001-6989-5971)
- Yingjie Xv (ORCID: https://orcid.org/0000-0001-5585-6058)
- Fangtong Liao
- Zongjie Wei
- Yong Chen (ORCID: https://orcid.org/0000-0002-9677-5710)
- Quanhao He (ORCID: https://orcid.org/0000-0002-8231-8199)
- Canjie Peng
Institutions
- Chongqing University (CN)
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Fuling Center Hospital of Chongqing (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-01-10
- DOI
- https://doi.org/10.3389/fonc.2024.1469427
- Citations
- 1
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Chongqing Medical University