Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study

作者：Fei-Xiang Xiong, Lei Sun, Xuejie Zhang, Jialiang Chen, Yang Zhou, Xunming Ji, Peipei Meng, Tong Wu, Xianbo Wang, Yixin Hou · 发表于：World Journal of Gastroenterology · 年份：2025 · DOI：10.3748/wjg.v31.i9.101383 · 被引用次数：7 · 研究领域：Liver Disease Diagnosis and Treatment、Hepatocellular Carcinoma Treatment and Prognosis、Liver physiology and pathology

BACKGROUND The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis. AIM To develop a machine learning-based diagnostic model for advanced liver fibrosis in NASH patients. METHODS A total of 749 patients who underwent liver biopsy at Beijing Ditan Hospital, Capital Medical University, between January 2010 and January 2020 were included. Patients were randomly divided into training (n = 522) and validation (n = 224) cohorts. Five machine learning models were applied to predict advanced liver fibrosis, with feature selection based on Shapley Additive Explanations (SHAP). The diagnostic performance of these models was compared to traditional scores such as the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis index based on the 4 factors (FIB-4), using metrics including the area under the receiver operating characteristic curve (AUROC), decision curve analysis (DCA), and calibration curves. RESULTS The Extreme Gradient Boosting (XGBoost) model outperformed all other machine learning models, achieving an AUROC of 0.934 (95%CI: 0.914-0.955) in the training cohort and 0.917 (95%CI: 0.880-0.953) in the validation cohort (P < 0.001). Incorporating liver stiffness measurement into the model further improved its performance, with an AUROC of 0.977 (95%CI: 0.966-0.980) in the training cohort and 0.970 ...