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Glioma grading is critical to clinical prognosis and survival prediction. In this paper, we propose a noninvasive method which combines radiomics and deep learning features to conduct glioma grading. By integrating radiomics features with high-level deep learning features, a more comprehensive representation of the images was constructed. First, different types of radiomics features were extracted from region of interest (ROI) of the image. A VGG-16 model pretrained on ImageNet was finetuned for the extraction of the deep learning features. Subsequent feature selection was performed to obtain three kinds of optimal feature subsets: radiomics features only, deep learning features only, and combined features. Finally, these feature subsets were used to train three classifiers including logistic regression (LR), support vector machine (SVM) and linear discriminant analysis (LDA) to predict the glioma grade. Our …