Differentiating benign and malignant myxoid soft tissue tumors based on multiparametric MRI radiomics and deep learning models
Objective To observe the value of multiparametric MRI-based radiomics model and deep learning(DL)model for distinguishing benign and malignant myxoid soft tissue tumors(MSTT).Methods A total of 141 MSTT patients confirmed with pathology were retrospectively collected.The patients were randomly divided into training set(n=98,including 51 cases of malignant MSTT and 47 cases of benign MSTT)and test set(n=43,including 22 cases of malignant MSTT and 21 cases of benign MSTT)at the ratio of 7∶3.Based on T1WI and fat suppression(FS)-T2WI in training set,radiomics features and DL features were extracted and selected,then a radiomics model and a DL model were constructed,respectively.Receiver operating characteristic(ROC)curves,calibration curves and decision curves were drawn,and the discrimination,calibration and net benefit of these 2 models were compared.Results In training set,the radiomics model for differentiating benign and malignant MSTT was constructed according to 9 optimal radiomics features,including 2 first order features,1 shape feature,3 gray level co-occurrence matrix features,1 gray level dependence matrix feature and 2 gray level size zone matrix features,while DL model was built based on 7 optimal DL features.In test set,the area under the ROC curve of radiomics model and DL model was 0.758 and 0.911,respectively,the latter was higher than the former(P=0.017).Both models had good calibration,and DL model had higher overall net benefit.Conclusion Compared with radiomics model,DL model based on MRI had better ability to differentiating benign and malignant MSTT,also higher overall net benefit.
万方数据
维普
