Clinical decision system for renal cell carcinoma integrating interpretable machine learning algorithms
We established a favorable prediction for the occurrence of distal metastasis with the ML model. The nomogram based on XGB algorithm can contribute to identify high-risk patients and provide optimal clinical strategies.
Authors
- Tianhong Zhang (ORCID: https://orcid.org/0000-0003-3823-6687)
- Chao Yang (ORCID: https://orcid.org/0000-0002-9603-0655)
- Jun Pang (ORCID: https://orcid.org/0000-0003-0024-9415)
- Yujie Zhang (ORCID: https://orcid.org/0000-0003-1245-0052)
- Tian Tian (ORCID: https://orcid.org/0009-0003-8903-0491)
- Ya Wang (ORCID: https://orcid.org/0000-0003-0767-7535)
- Qiliang Teng
- Yifan Zhang (ORCID: https://orcid.org/0000-0002-9190-3509)
Institutions
- Sun Yat-sen University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- The Seventh Affiliated Hospital of Sun Yat-sen University (CN)
- Xuzhou Central Hospital (CN)
- Xiamen Chang Gung Hospital (CN)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3389/fsurg.2025.1588208
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00