Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2202755 |
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| author | Ziman Chen Tin Cheung Ying Jiaxin Chen Chaoqun Wu Liujun Li Hui Chen Ting Xiao Yongquan Huang Xuehua Chen Jun Jiang Yingli Wang Wuzhu Lu Zhongzhen Su |
| author_facet | Ziman Chen Tin Cheung Ying Jiaxin Chen Chaoqun Wu Liujun Li Hui Chen Ting Xiao Yongquan Huang Xuehua Chen Jun Jiang Yingli Wang Wuzhu Lu Zhongzhen Su |
| author_sort | Ziman Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d702a13c8ef944968132bfef99e8a4ea |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-d702a13c8ef944968132bfef99e8a4ea2025-08-20T03:53:07ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492023-12-0145110.1080/0886022X.2023.2202755Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney diseaseZiman Chen0Tin Cheung Ying1Jiaxin Chen2Chaoqun Wu3Liujun Li4Hui Chen5Ting Xiao6Yongquan Huang7Xuehua Chen8Jun Jiang9Yingli Wang10Wuzhu Lu11Zhongzhen Su12Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaCentral Lab, Liver Disease Research Center, The Affiliated Hospital of Yunnan University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Radiology, The Second People’s Hospital of Shenzhen, Shenzhen, P.R. ChinaUltrasound Department, EDAN Instruments, Inc, Shenzhen, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaDepartment of Ultrasound, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, P.R. ChinaBackground 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.https://www.tandfonline.com/doi/10.1080/0886022X.2023.2202755Chronic kidney diseaserenal fibrosisshear wave elastographymultilayer perceptronmachine learning |
| spellingShingle | Ziman Chen Tin Cheung Ying Jiaxin Chen Chaoqun Wu Liujun Li Hui Chen Ting Xiao Yongquan Huang Xuehua Chen Jun Jiang Yingli Wang Wuzhu Lu Zhongzhen Su Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease Renal Failure Chronic kidney disease renal fibrosis shear wave elastography multilayer perceptron machine learning |
| title | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| title_full | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| title_fullStr | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| title_full_unstemmed | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| title_short | Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| title_sort | using elastography based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease |
| topic | Chronic kidney disease renal fibrosis shear wave elastography multilayer perceptron machine learning |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2202755 |
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