Yu Y X, Hu C H, Wang X M, Fan Y F, Hu M J, Shi C, Hu S, Zhu M, Zhang Y
Department of Radiology, the First Affiliated Hospital of Soochow University, Institute of Imaging Medicine, Soochow University, Suzhou 215006, China.
Zhonghua Yi Xue Za Zhi. 2021 May 11;101(17):1239-1245. doi: 10.3760/cma.j.cn112137-20200820-02425.
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. 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. 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 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. 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.
基于双期对比增强CT影像组学特征探讨机器学习模型在肝细胞癌（HCC）微血管侵犯（MVI）术前预测中的价值。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HCC患者[106例男性和42例女性，平均年龄（58±11）岁]的数据，其中MVI阳性88例，MVI阴性60例。按照7∶3的比例将患者随机分为训练集和验证集。采用MaZda软件提取HCC动脉期（AP）和门静脉期（PP）的三维（3D）影像组学特征，并结合三种特征选择方法（FPM法）和Lasso回归获得最优特征子集。然后，使用六种机器学习方法构建预测模型。绘制受试者工作特征（ROC）曲线评估上述模型的预测能力，并计算曲线下面积（AUC）、准确率、灵敏度和特异度。通过MaZda软件提取AP和PP期HCC的影像组学特征，每期各239个。FPM法和Lasso回归分别在AP期选出7个最优特征，PP期选出14个最优特征。验证集中基于AP期7个最优特征的决策树、极限梯度提升、随机森林、支持向量机（SVM）、广义线性模型和神经网络的AUC分别为0.736、0.910、0.913、0.915、0.897、0.648。验证集中SVM的AUC最高，准确率、灵敏度和特异度分别为95.35%、95.83%和94.74%。同样，验证集中基于PP期14个最优特征的机器学习模型预测HCC中MVI的AUC分别为0.873、0.876、0.913、0.859、0.877、0.834，差异均无统计学意义（均P>0.05）。验证集中随机森林的AUC最高，准确率、灵敏度和特异度分别为90.70%、87.50%和94.74%。基于双期增强CT影像组学特征的机器学习模型可用于HCC中MVI的术前预测，尤其是SVM和随机森林模型具有较高的预测效率。
