A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas

Purpose: To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas. Method: One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models. Results: For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919). Conclusions: The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.