A mRMRMSRC feature selection method for radiomics approach.

Radiomics can convert digital images to mineable data by extracting a huge number of image features. Because of the high dimensions of radiomics features, feature selection is a very important step which affects the performance of the final prediction or classification. In this paper, we propose a feature selection criterion for radiomics analysis of glioma based on Magnetic Resonance Image (MRI). The proposed method named as minimum Redundancy, Maximum Relevance and Maximum Sparse Representation Coefficient (mRMRMSRC) criterion, which take three factors into consideration at the same time: relevance between features and labels with or without the influence of all other features, and redundancy between each couple of features. Different from traditional feature selection method, the mRMRMSRC manifests the best performance compared with the methods based on sparse representation coefficient (SRC), minimum redundancy maximum relevance (mRMR), F_score and ReliefF. We conducted our methods on glioma Isocitrate Dehydrogenase 1 (IDH1) estimation. The experiment showed that mRMRMSRC produced area under the ROC curve (AUC) of 90% compared with 77%-89% of state-of-art methods.