Abstract
BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associated with an unfavourable prognosis. Therefore, timely diagnosis of LC in CHB patients is crucial. OBJECTIVE: This study aimed to enhance the diagnostic accuracy of LC in CHB patients by integrating liver stiffness measurement (LSM) with traditional indicators. METHODS: The study participants were randomly divided into training and internal validation sets. Employing the least absolute shrinkage and selection operator (LASSO) and random forest-recursive feature elimination (RF-RFE) for feature selection, we developed both traditional logistic regression and five machine learning models (k-nearest neighbors, random forest (RF), artificial neural network, support vector machine and eXtreme Gradient Boosting). Performance evaluation included receiver operating characteristic curves, calibration curves and decision curve analysis. Shapley additive explanations (SHAP) was employed to improve the interpretability of the optimal model. RESULTS: We retrospectively included 1609 patients with CHB, among whom 470 were diagnosed with cirrhosis. Cirrhosis was diagnosed based on histological confirmation or clinical assessment, supported by characteristic findings on abdominal ultrasound and corroborative evidence such as thrombocytopenia, varices or imaging from CT/MRI. In the internal validation, the RF model achieved an accuracy above 0.80 and an AUC above 0.80, with outstanding calibration ability and clinical net benefit. Additionally, the model exhibited excellent predictive performance in an independent external validation set. The SHAP analysis indicated that LSM contributed the most to the model. The model still showed strong discriminative power when using only LSM or traditional indicators alone. CONCLUSIONS: Machine learning models, especially the RF model, can effectively identify LC in CHB patients. Integrating LSM with traditional indicators can enhance diagnostic performance.