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Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

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第一作者： Céline,De Looze

第一单位： Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.;School of Engineering, Trinity College Dublin, Dublin, Ireland.

作者： Céline,De Looze ^[1] ; Alan,Beausang ^[2] ; Jane,Cryan ^[2] ; Teresa,Loftus ^[3] ; Patrick G,Buckley ^[4] ; Michael,Farrell ^[2] ; Seamus,Looby ^[5] ; Richard,Reilly ^[6] ; Francesca,Brett ^[2] ; Hugh,Kearney ^[7]

作者单位： Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.;School of Engineering, Trinity College Dublin, Dublin, Ireland. ^[1] Department of Neuropathology, Beaumont Hospital, Dublin, Ireland. ^[2] Department of Molecular Pathology, Beaumont Hospital, Dublin, Ireland. ^[3] Department of Molecular Pathology, Beaumont Hospital, Dublin, Ireland.;Genomics Medicine Ireland, Dublin, Ireland. ^[4] Department of Neuroradiology, Beaumont Hospital, Dublin, Ireland. ^[5] Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.;Institute of Neurosciences, Trinity College Dublin, Dublin, Ireland.;School of Medicine, Trinity College Dublin, Dublin, Ireland. ^[6] Department of Neuropathology, Beaumont Hospital, Dublin, Ireland. hugh.kearney.10@ucl.ac.uk. ^[7]

关键词 Diagnostic accuracy Glioma MRI Machine learning Random forest