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目的:探讨基于双期增强CT影像组学特征的机器学习模型术前预测肝细胞癌微血管侵犯（MVI）的价值。方法:回顾性分析2015年1月至2020年5月在苏州大学附属第一医院经病理确诊的148例［男106例，女42例，年龄（58±11）岁］肝细胞癌患者的资料，其中MVI阳性88例，MVI阴性60例。按照约7∶3的比例随机分配为训练集和验证集。利用MaZda软件提取肝细胞癌动脉期和门静脉期3D影像组学特征，采用3种特征选择方法联合（FPM法）和Lasso回归进行特征筛选，得到最优特征子集。然后用6种机器学习算法构建预测模型，采用受试者工作特征（ROC）曲线评估模型的预测能力，并计算出曲线下面积（AUC）、准确度、灵敏度和特异度。结果:MaZda软件提取肝细胞癌动脉期和门静脉期的影像组学特征，各239个。利用FPM法和Lasso 回归进行特征筛选可分别得到7个动脉期和14个门静脉期最优特征。基于动脉期的7个最优特征构建的决策树、极端梯度提升、随机森林、支持向量机、广义线性模型和神经网络等模型预测验证集肝细胞癌MVI的AUC值分别为0.736、0.910、0.913、0.915、0.897、0.648，其中支持向量机的AUC值最高，其准确度、灵敏度和特异度分别为95.35%、95.83%和94.74%。利用门静脉期的14个最优特征构建的上述机器学习模型预测验证集肝细胞癌MVI的AUC值分别为0.873、0.876、0.913、0.859、0.877、0.834，其差异均无统计学意义（均n P>0.05），其中随机森林模型的AUC值最高，其准确度、灵敏度和特异度分别为90.70%、87.50%和94.74%。n 结论:基于双期增强CT影像组学特征的机器学习模型可用于术前预测肝细胞癌微血管侵犯。其中，支持向量机和随机森林模型具有较高的预测效能。“,”Objective:To explore the value of machine learning models in preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on dual-phase contrast-enhanced CT radiomics features.Methods:The data of 148 patients [106 males and 42 females, with an average age of (58±11) years] with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 88 cases of positive MVI and 60 cases of negative MVI. According to the ratio of 7∶3, the patients were randomly divided into the training and validation sets, respectively. The three-dimensional (3D) radiomics features of HCC in arterial phase (AP) and portal venous phase (PP) were extracted by MaZda software, and the optimal feature subset was obtained by combining three feature selection methods (FPM method) and Lasso regression. Then, six machine learning methods were used to build the prediction models. Receiver operating characteristic (ROC) curves were drawn to evaluate the prediction ability of the aforementioned models, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated.Results:Radiomics features of HCC in AP and PP were extracted by MaZda software, with 239 in each phase. There were 7 optimal features in AP and 14 optimal features in PP selected by FPM method and Lasso regression, respectively. The AUCs of decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model, and neural network based on the 7 optimal features in AP in the validation set were 0.736, 0.910, 0.913, 0.915, 0.897, 0.648, respectively. The SVM had the highest AUC in the validation set, with the accuracy, sensitivity and specificity of 95.35%, 95.83% and 94.74%, respectively. Likewise, the AUCs of machine learning models in prediction of MVI in HCC based on the 14 optimal features in PP in the validation set were 0.873, 0.876, 0.913, 0.859, 0.877, 0.834, respectively, and there were no significant differences (all n P>0.05). The random forest had the highest AUC in the validation set, with the accuracy, sensitivity and specificity of 90.70%, 87.50% and 94.74%, respectively.n Conclusion:Machine learning models based on dual-phase enhanced CT radiomics features can be used in preoperative prediction of MVI in HCC, particularly the SVM and random forest models have high prediction efficiency.