DCE-MRI radiomics models predict IDH mutation in adult diffuse gliomas

Motivation: IDH mutation status of glioma have important influence on its occurrence and prognosis. Goal(s): To build radiomics models in DCE-MRI for predicting IDH mutation in adult diffuse gliomas. Approach: Several groups of features were extracted through multiparametric image: 1) Automatically calculated DCE-MRI metrics; 2) Structural MRI radiomics features, and 3) DCE-MRI radiomics features. Z-score normalization was used for feature normalization. Mann–Whitney U test was used for tumor selection. Stochastic gradient descent was used for machine learning classifier. Results: We achived an AUC of 0.874 for model combing structural-MRI, DCE-MRI radiomics and automatically-calculated DCE-MRI metrics. Impact: DCE-MRI radiomics models demonstrated great potential to predict IDH mutation status in Gliomas.