As grade Ⅱ and Ⅲ gliomas are difficult to distinguish in preoperative, this study attempted to find the best perfusion parameters for identifying grade Ⅱ/Ⅲ glioma by machine learning model. The machine learning model showed robust performance when using the parameters of volume transfer coefficient (Ktrans) and mean transit time (MTT) derived from the dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) imaging, which indicated that the combination of DCE and DSC perfusion techniques is expected to further improve the differential diagnosis of grade Ⅱ and Ⅲ gliomas.
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