Machine learning in preoperative glioma MRI: Survival associations by perfusion-based support vector machine outperforms traditional MRI

Purpose To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion‐based dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) for preoperative survival associations in patients with gliomas and compare our results to...

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Veröffentlicht in:	Journal of magnetic resonance imaging 2014-07, Vol.40 (1), p.47-54
Hauptverfasser:	Emblem, Kyrre E., Due-Tonnessen, Paulina, Hald, John K., Bjornerud, Atle, Pinho, Marco C., Scheie, David, Schad, Lothar R., Meling, Torstein R., Zoellner, Frank G.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Aged, 80 and over Algorithms Brain Neoplasms - mortality Brain Neoplasms - pathology Brain Neoplasms - surgery computer aided diagnosis (CAD) Female glioma Glioma - mortality Glioma - pathology Glioma - surgery histogram analysis Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Magnetic Resonance Angiography - methods Magnetic resonance imaging Male Middle Aged perfusion MRI Preoperative Care - methods Prognosis Reproducibility of Results Retrospective Studies Sensitivity and Specificity Support Vector Machine survival associations Survival Rate Treatment Outcome Young Adult
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