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.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Pizzini, Francesca B;Ghimenton, Claudio;
2020-01-01

Abstract

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.
2020
Diagnostic machine learning; Glioma stratification; Isocitrate dehydrogenase; DSC-MRI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1021199
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