【摘要】 目的 探讨基于术前钆塞酸二钠增强MRI的2018版肝脏影像报告和数据系统（LI-RADS v2018）及其他影像特征同时预测肝细胞癌（HCC）血管包绕肿瘤团簇（VETC）及微血管侵犯（MVI）的价值，进一步构建预测模型并评估模型风险分层能力。方法 回顾性纳入接受根治性肝切除术的232例HCC病人。根据VETC和MVI状态将HCC病人分为VETC及MVI均阳性组46例[VM（+）组]和VETC或MVI阴性组186例[非VM（+）组]。采用多因素Logistic回归分析确定VM(+)HCC的独立预测因素并构建联合模型。采用受试者操作特征（ROC）曲线评估单一预测因素及联合模型的预测效能，并计算ROC曲线下面积（AUC）、敏感度、特异度。通过DeLong检验比较单一预测因素及联合模型间AUC的差异。选择AUC最高的预测因素或模型进行生存分析，基于ROC曲线的最大约登指数设定预测概率截断值，将HCC病人分为高、低风险组。使用Kaplan-Meier生存曲线评估高与低风险组、VM（+）与非VM（+）组之间的无复发生存期（RFS）及早期复发（ER）风险。结果 多因素Logistic回归分析显示，肿瘤最大径、动脉期瘤周强化及肝胆期瘤周低信号是VM(+)HCC的独立预测因素，联合上述3个因素构建的联合模型的AUC、敏感度及特异度分别为0.792、80.4%、74.2%。Delong检验显示联合模型预测VM(+)HCC的AUC高于各单一预测因素（均P<0.05）。Kaplan-Meier生存分析表明VM（+）组较非VM（+）组的RFS更短、ER风险更高，高风险组较低风险组病人的RFS更短、ER风险更高（P<0.05）。结论 基于肿瘤最大径、动脉期瘤周强化及肝胆期瘤周低信号的联合模型可用于VM(+)HCC的术前预测。VETC及MVI同时存在与HCC病人切除术后ER风险增加及RFS降低相关。更多还原

【Abstract】 Objective To investigate the value of the 2018 Liver Image Reporting and Data System(LI-RADS v2018and other imaging features based on preoperative Gd-EOB-DTPA-enhanced MRI in predicting vascular encapsulating tumor clusters(VETC) and microvascular invasion(MVI) in hepatocellular carcinoma(HCC), constructing a predictive model and assessing its risk stratification capability. Methods This study retrospectively included 232 HCC patients who underwent curative liver resection. Based on the VETC and MVI status, patients were categorized into the VETC and MVI positive HCC group [VM(+) group](46 patients) and the VETC or MVI negative HCC group [non-VM(+) group](186 patients).Multivariate logistic regression analysis was used to identify the independent predictors of VM(+) HCC and to construct a combined model. The predictive efficacy of single predictors and the combined model was assessed using receiver operating characteristic(ROC) curves, and the area under the curve(AUC), sensitivity, and specificity were calculated. The predictor or model with the highest AUC was selected for survival analysis, and a statistical cutoff value for predictive probability was set based on the maximum Yoden index of the ROC curve to categorize HCC patients into high and low risk groups. Kaplan-Meier survival curves were used to evaluate recurrence-free survival(RFS) and early recurrence(ER) between the high-risk and low-risk groups, as well as between VM(+) and non-VM(+) HCC patients. Results Multivariate logistic regression analysis revealed that tumor size, peritumoral enhancement during the arterial phase, and peritumoral hypointensity during the hepatobilary phase were independent predictors of VM(+) HCC. The combined model incorporating these three factors achieved an AUC of 0.792, with sensitivity of 80.4% and specificity of 74.2%. DeLong’s test showed that the AUC of the combined model for predicting VM(+) HCC was higher than that of any single predictor(all P<0.05). Kaplan-Meier survival analysis demonstrated the RFS was shorter and the ER risk was higher in the VM(+) group compared to the non-VM(+) group,and similarly,the high-risk group predicted by the combined model had shorter RFS and higher ER risk than the low-risk group(P<0.05). Conclusion The combined model based on tumor size, peritumoral enhancement during the arterial phase,and peritumoral hypointensity during the hepatobilary phase can be used for preoperative prediction of VM(+) HCC. The coexistence of VETC and MVI is associated with an increased risk of ER and decreased RFS following HCC resection.更多还原