Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K‐means clustering
The K-means clustering algorithm enhanced by the Transformer feature transformation proposed in this study demonstrates promising accuracy and interpretability in predicting the overall survival rate after kidney tumor resection. This method provides a valuable tool for clinical decision-making and contributes to improved management and treatment strategies for patients with kidney tumors.
Authors
- Huaiwen Zhang (ORCID: https://orcid.org/0000-0002-3183-9218)
- Yiheng Hu (ORCID: https://orcid.org/0000-0002-6513-9834)
- Shouying Chen
- Yunfei Li
- Yiren Wang (ORCID: https://orcid.org/0000-0001-6757-5923)
- Zhongjian Wen
- Ping Zhou (ORCID: https://orcid.org/0000-0001-7454-210X)
- Haowen Pang
Institutions
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Jiangxi Provincial Cancer Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1002/mp.17639
- Citations
- 3
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Southwest Medical University