Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.
|Titolo:||Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status|
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||01.01 Articolo in Rivista|