Application of Nomogram model based on contrast-enhanced ultrasound parameters in predicting differentiation of hepato-cellular carcinoma
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® software.Methods A total of 128 patients 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® 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® software were assessed in terms of the pathological classification of HCC.A logistic regression model and nomo-gram prediction model were constructed.Receiver operating characteristic(ROC)curve analysis,calibration curve anal-ysis,and decision curve analysis(DC A)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 multivari-ate binary regression was Y=-2.360+1.674x1+1.019x2+0.753x3(2)+1.570x3(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 con-structed 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.
万方数据
维普
