基于增强CT术前诊断增殖型肝细胞癌的临床价值及预后分析  

Clinical value and prognosis analysis of enhanced CT preoperative diagnosis for proliferative hepatocellular carcinoma

作　　者：成杰 魏晓凡 陈伟[2] 叶小龙 张维[1] CHENG Jie;WEI Xiaofan;CHEN Wei;YE Xiaolong;ZHANG Wei(Department of Radiology,the Second Affiliated Hospital of Chongqing Medical University,Chongqing;Department of Radiology,Research Center for 7T Magnetic Resonance Translational Medicine,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,China;Department of Pathology,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,China)

机构地区：[1]重庆医科大学附属第二医院放射科,重庆 [2]陆军军医大学(第三军医大学)第一附属医院7T磁共振转化医学研究中心/放射科,重庆 [3]陆军军医大学(第三军医大学)第一附属医院病理科,重庆

出　　处：《陆军军医大学学报》2025年第7期708-719,共12页Journal of Army Medical University

基　　金：重庆市自然科学基金重点项目(CSTB2024NSCQ-KJFZZDX0036)。

摘　　要：目的基于增强CT影像特征构建增殖型肝细胞癌(hepatocellular carcinoma,HCC)的术前预测模型并进行预后分析。方法采用回顾性病例-对照研究方法。共纳入经病理证实为HCC的患者603例;其中陆军军医大学第一附属医院519例,以7︰3的比例随机分为训练组(n=363)和内部验证组(n=156);将重庆医科大学附属第二医院84例患者作为外部验证组。患者术前均行腹部CT增强检查,观察增殖型与非增殖型HCC患者临床资料情况及CT影像学特征。采用二元Logistic回归分析筛选增殖型HCC的独立危险因素,构建列线图预测模型,通过受试者工作特征曲线(receiver operating characteristic curve,ROC)、ROC曲线下面积(area under curve,AUC)评价其诊断效能,绘制校准曲线、决策曲线(decision curve analysis,DCA)评估列线图模型的校准性能和临床应用价值,并在内部验证组和外部验证组对模型进行验证。使用Kaplan-Meier生存曲线对比增殖型和非增殖型HCC的预后。结果多因素分析结果示乙型肝炎病毒脱氧核糖核酸(hepatitis B virus deoxyribonucleic acid,HBV-DNA)定量阴性、肿瘤包膜不完整、瘤内坏死或缺血(≥20%)、动脉期环形高强化是预测增殖型HCC的独立预测因子(P<0.05)。基于以上临床影像特征绘制预测增殖型HCC的列线图,在训练组AUC为0.805(95%CI:0.756~0.854),敏感度83.19%,特异度64.80%;在内部验证组中,AUC为0.793(95%CI:0.721~0.854),敏感度67.86%,特异度75.00%;在外部验证组中,AUC为0.842(95%CI:0.746~0.912),敏感度72.41%,特异度90.91%;校准曲线、DCA曲线示列线图模型的校准性能和临床适用性均较好。经病理证实/预测模型诊断的增殖型HCC患者术后无病生存期(disease free survival,DFS)均显著低于非增殖型HCC(训练组:P=0.005,P<0.001;内部验证组:P=0.006,P=0.006;外部验证组:P=0.002,P=0.015)。结论基于临床与影像特征构建的预测模型能较准确术前诊断增殖型HCC,且增殖型HCC预后较�Objective To construct a preoperative prediction model for proliferative hepatocellular carcinoma(HCC)based on enhanced CT image features,and to analyze the prognosis of the disease.Methods A retrospective case-control study was conducted on 603 patients with pathologically confirmed HCC.Among them,519 cases from the First Affiliated Hospital of Army Medical University were randomly divided into a training group(n=363)and an internal verification group(n=156)in a ratio of 7:3.Another 84 patients from the Second Affiliated Hospital of Chongqing Medical University served as an external validation group.All patients underwent abdominal CT scan with contrast before surgery.The clinical data and CT imaging characteristics of proliferative and non-proliferative HCC patients were observed.Binary logistic regression analysis was used to identify the independent risk factors of proliferative HCC,and a nomogram prediction model was constructed.Receiver operating characteristic(ROC)curve was plotted to evaluate its diagnostic performance,and calibration curve and decision curve analysis(DCA)were applied to evaluate its calibration performance and clinical application value.The model was validated in both the internal and external validation groups.Kaplan-Meier survival curves were employed to compare the prognosis between proliferative and non-proliferative HCC.Results Multivariate analysis showed that negative result of HBV-DNA quantification,incomplete tumor capsule,intratumoral necrosis or ischemia(≥20%),and annular hyperenhancement in arterial phase were independent predictors in predicting proliferative HCC(P<0.05).Our nomogram model for predicting proliferative HCC based on the above clinical imaging features had an AUC value of 0.805(95%CI:0.756~0.854),a sensitivity of 83.19%and a specificity of 64.80%in the training group.For the internal validation group,the AUC value was 0.793(95%CI:0.721~0.854),the sensitivity was 67.86%,and the specificity was 75.00%.In the external validation group,the AUC value was 0.842(95%

关 键 词：增殖型 肝细胞癌 X线计算机体层摄影术 预后研究 

分 类 号：R730.7[医药卫生—肿瘤] R735.7[医药卫生—临床医学] R814.42