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

ABSTRACT:

Background

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.

Methods

Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and r

SUBMITTER: Sudre CH 

PROVIDER: S-EPMC7336404 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature