[Objective]To construct an MRI-based radiomics model to differentiate high-grade gliomas(HGG)from low-grade gliomas(LGG).[Methods]Retrospectively,120 patients with surgically pathologically confirmed gliomas were collected and randomly divided into a training set(n=84)and a test set(n=36)according to a 7∶3 ratio.Enrolled patients underwent preoperative CE-T1WI and T2 FLAIR serial scans.Tumors were manually segmented using ITK-SNAP software to form regions of interest(ROIs).Radiomics features were screened using least absolute shrinkage and selection operator(LASSO),and the radiomics prediction model was built by support vector machine(SVM)learning algorithm.The performance of the prediction model was evaluated by plotting receiver operating characteristic(ROC)curve to obtain area under the curve(AUC),sensitivity,specificity and accuracy.[Results]From the CE-T1WI and T2 FLAIR sequences,1835 radiomics features were extracted respectively,and 10 and 6 features were screened from each sequence after the redundancy and decontamination process.Among the single sequence models constructed by SVM,the CE-T1WI model has better prediction performance.It has AUC values of 0.898 and 0.845 for the training and test sets,respectively.The combined model CE-T1WI+T2 FLAIR outperforms the single sequence model with AUCs of 0.952 and 0.818 in the training and test sets,respectively.[Conclusion]Radiomics diagnostic models constructed based on MRI radiomics have certain diagnostic efficacy in predicting the high and low grades of gliomas,among which the combined model CE-T1WI+T2 FLAIR has the highest predictive diagnostic value.
万方数据
维普
