ABSTRACT
Objective To explore the value of dynamic nomogram constructed by multi spiral computed tomography(MSCT)features combined with inflammatory indicators in predicting the status of microvascular invasion(MVI)of hepatocellular carcinoma(HCC)before surgery.Methods The clinical and imaging data of 137 patients with postoperative pathologically confirmed HCC were analyzed retrospectively.According to the status of the MVI,they were divided into positive group(44 cases)and negative group(93 cases).Multivariate logistic regression analysis was used to screen independent risk factors for predicting the MVI status of HCC patients,and a joint prediction model was constructed,which was displayed in the form of a dynamic nomogram.The receiver operating characteristic(ROC)curve,calibration curve and Hosmer-Lemeshow test were used to evaluate the diagnostic efficiency,calibration and goodness of fit of the model,Akaike information criterion(AIC)and Bayesian information criterion(BIC)were used for comparison between the models,and a 5-fold cross-validation and decision curve analysis(DCA)were also used to evaluate the stability and clinical applicability of the model.Results Multivariate logistic regression analysis showed that necrosis and delayed-phase enhancement(DEd),and alkaline phosphatase to lymphocyte ratio(ALR)were independent risk factors for predicting MVI status in HCC patients.The area under the curve(AUC)of the dynamic nomogram was 0.721,with the sensitivity of 0.705 and the specificity of 0.656.The AIC and BIC values were 152.372 and 158.212,respectively.The calibration curve and the Hosmer-Lemeshow test showed that the model had a high degree of calibration and goodness of fit(χ2=2.372,P=0.967),the average AUC of the 5-fold cross-validation was 0.787,and the DCA showed that the nomogram model had a good clinical applicability.Conclusion The dynamic nomogram model constructed by MSCT features combined with inflammatory indicators is feasible to predict the MVI status of HCC patients before surgery,and the dynamic nomogram can directly generate the prediction results of different individuals.
