基于超声造影参数构建列线图模型预测肝细胞癌分化程度的应用研究  

Application of Nomogram model based on contrast-enhanced ultrasound parameters in predicting differentiation of hepatocellular carcinoma

作　　者：廉淑敏 程洪晶 李红晶 王辉[1] LIAN Shumin;CHENG Hongjing;LI Hongjing;WANG Hui(Department of Ultrasound,China-Japan Union Hospital of Jilin University,Changchun 130033,China)

机构地区：[1]吉林大学中日联谊医院超声科,吉林长春130033

出　　处：《中国实验诊断学》2024年第10期1145-1150,共6页Chinese Journal of Laboratory Diagnosis

基　　金：吉林省卫生人才专项项目资助(2023SCZ59)。

摘　　要：目的通过软件定量分析超声造影灌注参数与原发性肝细胞癌(HCC)病理分级间的相关性,进而预测肝细胞癌的病理分化程度。方法收集在吉林大学中日联谊医院接受超声造影检查(CEUS)并通过肝穿刺活检且病理证实为原发性肝细胞癌的患者128例,以Edmondson-Steiner病理分级为金标准,将其分为低级别组和高级别组。分析B型超声(BMUS)和CEUS的特征。通过软件进行DCE-US分析得到定量参数,并与HCC病理分级进行对比分析研究,构建logistic回归方程及Nomogram预测模型,并通过绘制ROC曲线、校准曲线、DCA曲线来评价模型的诊断效果。结果单因素分析中mTTI、FT、单病灶最大直径大小具有统计学差异(P<0.05)。多因素二元回归得到的方程为:Y=-2.360+1.674χ_(1)+1.019χ_(2)+0.753χ_(3(2))+1.570χ_(3(3)),其AUC为0.831,敏感度为82.0%,特异度为79.5%。结论联合多参数构建的回归模型可有效提高CEUS对HCC不同病理分化程度的诊断效能,为CEUS作为术前诊断HCC病理分化程度的影像学方法提供临床依据和数据支撑。Objective To predict the degree of pathological differentiation of hepatocellular carcinoma(HCC)by Quantitative analysis the correlation between the perfusion parameters of contrast-enhanced ultrasound(CEUS)and the pathological grades of HCC using VueBox^((R)) software.Methods A total of 128patients with hepatocellular carcinoma(HCC)confirmed by pathology underwent contrast-enhanced ultrasonography(CEUS)and liver biopsy in China-Japan Union Hospital of Jilin University.The Edmondson-Steiner pathological classification system was used as the gold standard for dividing the patients into the low-grade and high-grade groups.CEUS was performed with the SonoVue○R contrast agent to analyze the B-mode ultrasound(BMUS)features and the CEUS enhancement patterns of the patients.The quantitative parameters obtained from dynamic contrast-enhanced ultrasonography(DCE-US)analysis using Vue-Box^((R)) Rsoftware were assessed in terms of the pathological classification of HCC.A logistic regression model and nomogram prediction model were constructed.Receiver operating characteristic(ROC)curve analysis,calibration curve analysis,and decision curve analysis(DCA)were performed to evaluate the diagnostic performance of the models.Results According to univariate analysis,the mean transit time(mTTI),fall time(FT),and maximum diameter of single lesions significantly different between the low-grade and high-grade groups(P<0.05).The equation obtained from multivariate binary regression was Y=-2.360+1.674χ_(1)+1.019χ_(2)+0.753χ_(3(2))+1.570χ_(3(3)),which achieved an area under the ROC curve(AUC)of 0.831,a sensitivity of 82.0%,and a specificity of 79.5%.Conclusion The regression model constructed by combining multiple parameters can effectively improve the diagnostic performance of CEUS in predicting the pathological differentiation grade of HCC,thus providing a clinical basis and empirical support for the use of CEUS as a diagnostic imaging method for this disease.

关 键 词：超声造影 原发性肝细胞癌 Edmondson-Steiner病理分级 

分 类 号：R735.7[医药卫生—肿瘤]