Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Chen, Ziman; Ying, Tin Cheung; Chen, Jiaxin; Wu, Chaoqun; Li, Liujun; Chen, Hui; Xiao, Ting; Huang, Yongquan; Chen, Xuehua; Jiang, Jun; Wang, Yingli; Lu, Wuzhu; Su, Zhongzhen
• Is Part Of: Renal failure. Volume 45:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Chronic kidney disease -- renal fibrosis -- shear wave elastography -- multilayer perceptron -- machine learning; Chronic renal failure -- Periodicals; Acute renal failure -- Periodicals; Uremia -- Periodicals; 616.614005
• DOI: 10.1080/0886022X.2023.2202755
• Languages: English
• ISSNs: 0886-022X

Abstract

Abstract: Background: Given its progressive deterioration in the clinical course, noninvasive assessment and risk stratification for the severity of renal fibrosis in chronic kidney disease (CKD) are required. We aimed to develop and validate an end-to-end multilayer perceptron (MLP) model for assessing renal fibrosis in CKD patients based on real-time two-dimensional shear wave elastography (2D-SWE) and clinical variables. Methods: From April 2019 to December 2021, a total of 162 patients with CKD who underwent a kidney biopsy and 2D-SWE examination were included in this single-center, cross-sectional, and prospective clinical study. 2D-SWE was performed to measure the right renal cortex stiffness, and the corresponding elastic values were recorded. Patients were categorized into two groups according to their histopathological results: mild and moderate-severe renal fibrosis. The patients were randomly divided into a training cohort ( n = 114) or a test cohort ( n = 48). The MLP classifier using a machine learning algorithm was used to construct a diagnostic model incorporating elastic values with clinical features. Discrimination, calibration, and clinical utility were used to appraise the performance of the established MLP model in the training and test sets, respectively. Results: The developed MLP model demonstrated good calibration and discrimination in both the training [area under the receiver operating characteristic curve (AUC) = 0.93; 95% confidence interval (CI) = 0.88 to 0.98] and test cohorts [AUC = 0.86; 95% CI = 0.75 to 0.97]. A decision curve analysis and a clinical impact curve also showed that the MLP model had a positive clinical impact and relatively few negative effects. Conclusions: The proposed MLP model exhibited the satisfactory performance in identifying the individualized risk of moderate-severe renal fibrosis in patients with CKD, which is potentially helpful for clinical management and treatment decision-making.