Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques
This study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.
Authors
- Shitian Li (ORCID: https://orcid.org/0009-0002-9082-4339)
- Yali Tang (ORCID: https://orcid.org/0000-0003-0115-6667)
- Yuan Liu (ORCID: https://orcid.org/0000-0002-2578-0707)
- Yi Zhang (ORCID: https://orcid.org/0000-0002-5867-9322)
- Jianlin Li (ORCID: https://orcid.org/0000-0003-4713-5028)
- Zhu Liang (ORCID: https://orcid.org/0000-0001-8223-5003)
- Xiaoqi Sun (ORCID: https://orcid.org/0000-0003-4692-8309)
- Yao Lei (ORCID: https://orcid.org/0000-0002-4299-8011)
- Xinyang Fu
Institutions
- Kaiping Central Hospital (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e38242
- Citations
- 3
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.72