术前MRI特征对肝细胞癌血管包绕肿瘤团簇及微血管侵犯的评估  

Assessment of vascular encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma based on preoperative MRI features

作　　者：刘子鑫 闫祖仪 张涛[2,3] 张学琴[2,3] 顾春燕[4] 瞿琦 姜吉锋[2,3] LIU Zixin;YAN Zuyi;ZHANG Tao;ZHANG Xueqin;GU Chunyan;QU Qi;JIANG Jifeng(Nantong University,Nantong 226000,China;Department of Radiology,Nantong Third Hospital Affiliated to Nantong University;Department of Radiology,Nantong Third People’s Hospital;Department of Pathology,Nantong Third Hospital Affiliated to Nantong University)

机构地区：[1]南通大学,南通226000 [2]南通大学附属南通第三医院影像科 [3]南通市第三人民医院影像科 [4]南通大学附属南通第三医院病理科

出　　处：《国际医学放射学杂志》2024年第5期539-545,574,共8页International Journal of Medical Radiology

基　　金：江苏省研究生科研与实践创新计划项目(SJCX23_1800);江苏省研究型医院学会感染影像科研专项科技项目(GY202203);南通市科技计划项目(MS22022056)。

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。Objective To investigate the value of the 2018 Liver Image Reporting and Data System(LI-RADS v2018)and 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

关 键 词：肝细胞癌 微血管侵犯 磁共振成像 肝脏影像报告和数据系统 

分 类 号：R735.7[医药卫生—肿瘤] R445.2[医药卫生—临床医学]