Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance.

Purpose: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify...

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Publicado en:	Neuroradiology Vol. 63; no. 12; pp. 2047 - 2057
Autores principales:	Siakallis, Loizos, Sudre, Carole H., Mulholland, Paul, Fersht, Naomi, Rees, Jeremy, Topff, Laurens, Thust, Steffi, Jager, Rolf, Cardoso, M. Jorge, Panovska-Griffiths, Jasmina, Bisdas, Sotirios
Formato:	diagnostic images research tables/charts Journal Article
Publicado:	Springer Nature Dec2021
Acceso en línea:	Ver este registro en EBSCOhost