The value of Gd-EOB-DTPA enhanced MRI radiomics and machine learning in preoperative prediction of microvascular invasion of hepatocellular carcinoma / 中华放射学杂志

ABSTRACT

Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.