Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105835
Title: | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease | Authors: | Chen, Z Ying, TC Chen, J Wu, C Li, L Chen, H Xiao, T Huang, Y Chen, X Jiang, J Wang, Y Lu, W Su, Z |
Issue Date: | 2023 | Source: | Renal failure, 2023, v. 45, no. 1, 2202755 | 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. |
Keywords: | Chronic kidney disease Machine learning Multilayer perceptron Renal fibrosis Shear wave elastography |
Publisher: | Taylor & Francis Inc. | Journal: | Renal failure | ISSN: | 0886-022X | EISSN: | 1525-6049 | DOI: | 10.1080/0886022X.2023.2202755 | Rights: | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open access article distributed under the terms of the Creative Commons attribution license (http://creativecommons.org/licenses/by/4.0/), which permits unre-stricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. The following publication Chen, Z., Ying, T. C., Chen, J., Wu, C., Li, L., Chen, H., … Su, Z. (2023). Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease. Renal Failure, 45(1), 2202755 is available at https://doi.org/10.1080/0886022X.2023.2202755. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Chen_Using_Elastography-based_Multilayer.pdf | 2.59 MB | Adobe PDF | View/Open |
Page views
12
Citations as of Jun 30, 2024
Downloads
2
Citations as of Jun 30, 2024
SCOPUSTM
Citations
1
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
1
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.