Keywords: Tumors (Pre-Treatment), Tumor, DCE-MRI, IDH, Glioma, Machine learning
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.
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