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 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.

Development and Deployment of a Novel Diagnostic Tool Based on Conventional Ultrasound for Fibrosis Assessment in Chronic Kidney Disease

RATIONALE AND OBJECTIVES

Accurate identification of risk information about fibrosis severity is crucial for clinical decision-making and clinical management of patients with chronic kidney disease (CKD). This study aimed to develop an ultrasound (US)-derived computer-aided diagnosis tool for identifying CKD patients at high risk of developing moderate-severe renal fibrosis, in order to optimize treatment regimens and follow-up strategies.

MATERIALS AND METHODS

A total of 162 CKD patients undergoing renal biopsies and US examinations were prospectively enrolled and randomly divided into training (n = 114) and validation (n = 48) cohorts. A multivariate logistic regression approach was employed to develop the diagnostic tool named S-CKD for differentiating moderate-severe renal fibrosis from mild one in the training cohort by integrating the significant variables, which were screened out from demographic characteristics and conventional US features via the least absolute shrinkage and selection operator regression algorithm. The S-CKD was then deployed as both an online web-based and an offline document-based, easy-to-use auxiliary device. In both the training and validation cohorts, the S-CKD's diagnostic performance was evaluated through discrimination and calibration. The clinical benefit of using S-CKD was revealed by decision curve analysis (DCA) and clinical impact curves.

RESULTS

The proposed S-CKD achieved an area under the receiver operating characteristic curve of 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.81 (95% CI: 0.68-0.94) in the training and validation cohorts, respectively, indicating satisfactory diagnosis performance. Results of the calibration curves showed that S-CKD has excellent predictive accuracy (Hosmer-Lemeshow test: training cohort, p = 0.497; validation cohort, p = 0.205). The DCA and clinical impact curves exhibited a high clinical application value of the S-CKD at a wide range of risk probabilities.

CONCLUSION

The S-CKD tool developed in this study is capable of discriminating between mild and moderate-severe renal fibrosis in patients with CKD and achieving promising clinical benefits, which may aid clinicians in personalizing medical decision-making and follow-up arrangement.

Assessment of Renal Fibrosis in Patients With Chronic Kidney Disease Using Shear Wave Elastography and Clinical Features: A Random Forest Approach

OBJECTIVE

Renal fibrosis is the common pathological hallmark of chronic kidney disease (CKD) progression. In this study, a random forest (RF) classifier based on 2-D shear wave elastography (SWE) and clinical features for the differential severity of renal fibrosis in patients with CKD is proposed.

METHODS

A total of 162 patients diagnosed with CKD who underwent 2-D SWE and renal biopsy were prospectively enrolled from April 2019 to December 2021 and then randomized into training (n = 114) and validation (n = 48) cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machines (SVM-RFE) algorithm were employed to select renal fibrosis-related features from clinical information and elastosonographic findings. An RF model was subsequently constructed using the aforementioned informative parameters in the training cohort and evaluated in terms of discrimination, calibration and clinical utility in both cohorts.

RESULTS

The LASSO and SVM-RFE analyses revealed that age, sex, blood urea nitrogen, renal resistive index, hypertension and the 2D-SWE value were independent risk variables associated with renal fibrosis severity. The established RF model incorporating these six variables exhibited fine discrimination in both the derivation (area under the curve [AUC]: 0.84, 95% confidence interval [CI]: 0.76-0.91) and validation (AUC: 0.88, 95% CI: 0.77-0.98) cohorts. Moreover, the calibration curve revealed satisfactory predictive accuracy, and the decision curve analysis revealed a significant clinical net benefit.

CONCLUSION

The developed RF model, via a combination of the 2-D SWE value and clinical information, indicated satisfactory diagnostic performance and clinical practicality toward differentiating moderate-severe from mild renal fibrosis, which may provide critical insight into risk stratification for patients with CKD.