Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease
This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation.
Authors
- Jianying Niu (ORCID: https://orcid.org/0000-0001-6343-2319)
- Tian Tang (ORCID: https://orcid.org/0000-0003-2677-3426)
- Qiuling Fan (ORCID: https://orcid.org/0000-0003-2598-6075)
- Lehao Wu (ORCID: https://orcid.org/0000-0003-0127-2609)
- Ai Peng (ORCID: https://orcid.org/0000-0002-5311-1420)
- Yu Chen (ORCID: https://orcid.org/0000-0001-8678-3787)
- Yingying Zhang (ORCID: https://orcid.org/0000-0001-8794-364X)
- Xiaoqin Zhang (ORCID: https://orcid.org/0000-0002-0952-7445)
- Dan Zhao (ORCID: https://orcid.org/0000-0002-6154-2191)
- Chengsheng Luo
Institutions
- Tongji University (CN)
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- Shanghai Tenth People's Hospital (CN)
- Shanghai First People's Hospital (CN)
- Tongji Hospital (CN)
Publication Details
- Journal
- Renal Failure
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1080/0886022x.2025.2489715
- Citations
- 3
- Primary Topic
- Ultrasound and Hyperthermia Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Natural Science Foundation of China
- Shanghai Rising-Star Program