【摘要】 目的:建立一种基于磁共振质子密度脂肪分数(MRI-PDFF)的无创预测模型，以识别病情进展风险较高的代谢相关脂肪性肝炎(MASH)患者。方法:回顾性分析广东省中医院大学城医院127例经肝活检证实代谢相关脂肪性肝病(MASLD)患者的临床、影像及病理资料。采用单因素和多因素Logistic回归分析筛选独立预测因子，构建MASH预测模型。采用受试者工作特征曲线分析预测模型MASH患者的诊断效能，计算灵敏度≥90%排除截断点、特异度≥90%纳入截断点、阳性预测值和阴性预测值，并使用DeLong法比较该预测模型与其他临床指标的诊断效能差异。结果:基于MRI-PDFF、体质量指数和性别建立的联合模型预测MASH的曲线下面积为0.876,采用排除截断点，联合预测模型以66.6%的阴性预测值排除MASH患者，采用纳入截断点，联合预测模型以96.1%的阳性预测值纳入MASH患者。结论:基于性别、体质量指数和PDFF建立的MASH联合预测模型具有较高的诊断效能，采用双截断值，联合预测模型可有效识别进展风险较高的MASH患者，有助于临床决策的制定。更多还原

【Abstract】 Objective:To develop a noninvasive predictive model based on magnetic resonance proton density fat fraction(MRI-PDFF) for identifying patients with metabolic dysfunction-associated steatohepatitis(MASH) at elevated risk of disease progression.Methods:A retrospective analysis was conducted on clinical, imaging, and histopathological data from 127 patients diagnosed with metabolic dysfunction-associated steatotic liver disease(MASLD) by liver biopsy. Independent predictors of MASH were identified via univariate and multivariate logistic regression and used to construct the model. Diagnostic performance was assessed using receiver operating characteristic(ROC) analysis. Dual cutoff points were defined a rule-out threshold with sensitivity≥90% and a rule-in threshold with specificity≥90% and corresponding positive(PPV)and negative predictive values(NPV)were calculated. The model was compared with established clinical indices(APRI, NFS, FIB-4) using the DeLong test.Results:The union predictive model incorporating MRI-PDFF, BMI, and gender achieved an AUC of 0.876 for identifying MASH. At the rule-out threshold, an NPV of 66.6% was obtained; at the rule-in threshold, a PPV of 96.1% was achieved.Conclusion:A non-invasive union prediction model incorporating gender, BMI, and PDFF exhibits high diagnostic accuracy for MASH. Applying dual cutoff points enables effective risk stratification in MASLD, facilitating identification of higher-risk patients and supporting clinical decision-making.更多还原