Objective To explore the predictive value of a combined radiomics model and abdominal fat area parame-ters for microvascular invasion of hepatocellular carcinoma based on CT portal phase.Methods Retrospectively collected 134 cases confirmed by pathology exists Microvascular invasion of Hepatocellular carcinoma scan and enhanced CT images of patients.According to the principle of random stratified sampling,the patients were divided into a training set(n=93)and a testing set(n=41)with a ratio of 7∶3.Firstly,the abdominal fat area parameters of all patients were measured on CT images,and the fat model was constructed by univariate and multivariate Logistic regression.Then,radiomics features of portal venous tumors were extracted by an automated feature extraction algorithm.Spearman correlation analysis and the Least absolute shrinkage and selection operator were used for feature selection.Six kinds of machine learning models were constructed respectively.The better performance model combined with abdominal fat area parameters was used to construct the final combined model.The Area under the curve of the Receiver operating characteristic curve was used to evaluate the predictive efficacy of the model,and the calibration curve was used to verify the calibration capability.The Decision curve analysis is used to analyze and compare the clinical utility of models,and the Delong test assesses the differences in predic-tive power between models.Results Univariate and multivariate logistic regression indicated that Subcutaneous adipose tissue area and Visceral adipose tissue area parameters were independent risk factors for HCC-MVI,and the AUC values of the fat model constructed by this method were 0.747(0.648-0.845)and 0.696(0.536-0.857)in the training and test sets.After a series of feature screening and dimension reduction methods,The remaining 10 radiomics features were used for the construction of six machine learning models,the support vector machine model had better performance.In the training set and the testing set,the AUC values were 0.904(0.844-0.965)and 0.838(0.717-0.959),respectively.The joint model was constructed by combining the fat parameters and the radiomics model and visualized by using the nomo-gram.The performance of the combined model in the training set was 0.925(0.872-0.978),which was significantly higher than that of the radiomics model(P=0.029)and the fat model(P=0.0054).The performance of the combined model in the testing set was 0.877(0.772-0.983),which was significantly higher than that of the fat model(P=0.0165),which was higher than that of the radiomics model(P=0.207),the calibration curve showed that the model was well-fitted,and the analysis of the decision curve suggested that the joint model had better clinical practical value than other models.Conclusion The joint model based on radiomics features of the CT portal phase and the abdominal fat area has a high predictive value for HCC-MVI.
NETL
NSTL
万方数据
