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已发表论文
基于全身炎症综合指数和超声影像组学的慢性肾脏病肾纤维化七种人工智能辅助预测模型的比较
Authors Wu Y, Lu K, Liao Y, Wang J, Yang T, Wang L, Li F
Received 22 May 2025
Accepted for publication 16 October 2025
Published 27 October 2025 Volume 2025:18 Pages 6483—6496
DOI https://doi.org/10.2147/IJGM.S542130
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor David E. Stec
Yutao Wu,1,* Kun Lu,1,* Yixiang Liao,2 Jing Wang,1 Tao Yang,2 Li Wang,2 Fei Li1
1Department of Urology, The Second Hospital of Jingzhou, Jingzhou, Hubei, People’s Republic of China; 2Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Fei Li, Email lwf0070@163.com
Objective: Renal fibrosis, key in progressive chronic kidney disease (CKD), requires invasive biopsy for diagnosis. This study aimed to develop an optimized artificial intelligence (AI)-assisted model for pre-biopsy screening..
Methods: A multicenter retrospective study included 758 renal fibrosis patients from two tertiary hospitals. 515 from The Second Hospital of Jingzhou were split 7:3 into training and internal validation sets; 243 formed an external test set. Severity (mild/moderate-to-severe) was classified via Banff score. Convolutional neural networks (CNN) extracted features from renal ultrasound images; peripheral blood counts were collected. After Least Absolute Shrinkage and Selection Operator (LASSO) variable screening, machine learning (ML) models were built, evaluated via receiver operating characteristic (ROC) curve’s area under the curve (AUC) and decision curve analysis (DCA).
Results: The model combining ultrasound radiomics and Aggregate Index of Systemic Inflammation (AISI) showed AUC 0.71– 0.96 in training/internal validation, 0.89 in external test. Random Forest (RF) performed best (AUC 0.96 in training; 0.93/0.95 in internal/external validation).
Conclusion: The RF-based model effectively evaluates renal fibrosis in CKD patients. Integrating AISI and ultrasound radiomics offers a novel strategy for dynamic assessment and biopsy guidance.
Keywords: chronic kidney disease, artificial intelligence, renal fibrosis, machine learning, aggregate index of systemic inflammation, predictive model
1Department of Urology, The Second Hospital of Jingzhou, Jingzhou, Hubei, People’s Republic of China; 2Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Fei Li, Email lwf0070@163.com
Objective: Renal fibrosis, key in progressive chronic kidney disease (CKD), requires invasive biopsy for diagnosis. This study aimed to develop an optimized artificial intelligence (AI)-assisted model for pre-biopsy screening..
Methods: A multicenter retrospective study included 758 renal fibrosis patients from two tertiary hospitals. 515 from The Second Hospital of Jingzhou were split 7:3 into training and internal validation sets; 243 formed an external test set. Severity (mild/moderate-to-severe) was classified via Banff score. Convolutional neural networks (CNN) extracted features from renal ultrasound images; peripheral blood counts were collected. After Least Absolute Shrinkage and Selection Operator (LASSO) variable screening, machine learning (ML) models were built, evaluated via receiver operating characteristic (ROC) curve’s area under the curve (AUC) and decision curve analysis (DCA).
Results: The model combining ultrasound radiomics and Aggregate Index of Systemic Inflammation (AISI) showed AUC 0.71– 0.96 in training/internal validation, 0.89 in external test. Random Forest (RF) performed best (AUC 0.96 in training; 0.93/0.95 in internal/external validation).
Conclusion: The RF-based model effectively evaluates renal fibrosis in CKD patients. Integrating AISI and ultrasound radiomics offers a novel strategy for dynamic assessment and biopsy guidance.
Keywords: chronic kidney disease, artificial intelligence, renal fibrosis, machine learning, aggregate index of systemic inflammation, predictive model