Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.
Carole H SudreJasmina Panovska-GriffithsEser SanverdiSebastian BrandnerVasileios K KatsarosGeorge StranjalisFrancesca B PizziniClaudio GhimentonKatarina Surlan-PopovicJernej AvsenikMaria Vittoria SpampinatoMario NigroArindam R ChatterjeeArnaud AttyeSylvie GrandAlexandre KrainikNicoletta AnzaloneGian Marco ConteValeria RomeoLorenzo UggaAndrea ElefanteElisa Francesca CiceriElia GuadagnoEftychia KapsalakiDiana RoettgerJavier GonzalezTimothé BoutelierM Jorge CardosoSotirios BisdasPublished in: BMC medical informatics and decision making (2020)
Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.