Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease

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
• renal fibrosis
• shear wave elastography
• multilayer perceptron
• machine learning

Acknowledgement

We are grateful to Dr. Zhijuan Li for her valuable advice and professional guidance regarding the pathological analysis during the revision process. Additionally, we are grateful to Dr. Fukuan Shi for his professional advice regarding the collection of clinical indicators.

Ethical statement

This study protocol was approved by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-Sen University (protocol code K09-1; approval date: May 2019) and complied with the tenets of the Helsinki Declaration. Written informed consent to participate was obtained from all subjects.

Disclosure statement

All authors declare no conflict of interest.

Data availability statement

The data presented in this study are available from the corresponding author upon reasonable request. Data are not publicly available due to privacy or ethical concerns.

Additional information

Funding

The work was supported by the Natural Science Foundation of Guangdong Province [2018A0303130070]; National Natural Science Foundation of China [82072038].