背景：微血管侵犯（MVI）的预测可能有助于确定肝细胞癌（HCC）的治疗策略。 目的：基于术前多相CT影像开发放射组学方法以预测MVI状态，并确定与MVI相关的差异表达基因。 材料和方法：回顾性收集2012年5月至2020年9月间在四个医疗中心经病理证实的HCC患者。从术前对准或减影CT图像中的肿瘤和周围区域提取放射组学特征。在训练集中，应用逻辑回归进行特征约简后，使用这些特征构建了五个放射组学模型。采用内部和外部测试集对这些模型进行测试，根据病理学参考标准计算接受者操作特征曲线下面积（AUC）。最佳AUC放射组学模型和临床放射学特征结合建立混合模型。在结果队列（昆明中心），采用log-rank检验根据高低模型得分分析早期无复发生存率和总生存率。使用癌症图像档案中的RNA测序数据进行基因表达分析。 结果：共773名患者（年龄中位数59岁；IQR，49-64岁；633名男性）被分为训练集（n = 334）、内部测试集（n = 142）、外部测试集（n = 141）、结果队列（n = 121）和RNA测序分析集（n = 35）。放射组学模型和混合模型分别在内部测试集和外部测试集中的AUC分别为0.76和0.86，0.72和0.84。可使用混合模型分类早期无复发生存率（P <0.01）和总生存率（P <0.007）。结果为MVI阳性的患者中的差异表达基因涉及葡萄糖代谢。 结论：混合模型在预测MVI方面表现最佳。© RSNA，2023。本文提供补充资料。请参阅本期编辑Summers的社论。

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.