Key result
XGBoost model using clinical and elastosonographic features distinguishes moderate-severe from mild renal fibrosis with 0.97 AUC.
Why the study?
Non-invasive renal fibrosis assessment is critical for personalizing decision-making and follow-up in CKD, prompting the use of machine learning integrating clinical and elastosonographic features.
Does a machine learning model integrating clinical and elastosonographic features accurately detect moderate-severe renal fibrosis in Asian patients with CKD?
Cross-Sectional (n=162)
No
Does a machine learning model integrating clinical and elastosonographic features accurately detect moderate-severe renal fibrosis in Asian patients with CKD?
Effect estimate: AUC 0.97 (95% CI 0.94-0.99)
An XGBoost machine learning model integrating clinical and elastosonographic features accurately distinguishes moderate-severe from mild renal fibrosis in CKD patients.
May support non-invasive fibrosis staging in CKD; leaves open external validation before clinical adoption.
BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. METHODS: A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. RESULTS: The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. CONCLUSION: This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.
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Chen et al. (2024) conducted a cross-sectional in Chronic kidney disease (n=162). XGBoost machine learning model vs. SVM, LightGBM, and KNN models was evaluated on Area under the receiver operating characteristic curve (AUC) for distinguishing moderate-severe from mild renal fibrosis in the primary dataset (AUC 0.97, 95% CI 0.94-0.99). The XGBoost machine learning model integrating clinical and elastosonographic features accurately distinguished moderate-severe from mild renal fibrosis with an AUC of 0.97.
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