Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.

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Autores principales:	Nabil Elshafeey, Aikaterini Kotrotsou, Ahmed Hassan, Nancy Elshafei, Islam Hassan, Sara Ahmed, Srishti Abrol, Anand Agarwal, Kamel El Salek, Samuel Bergamaschi, Jay Acharya, Fanny E. Moron, Meng Law, Gregory N. Fuller, Jason T. Huse, Pascal O. Zinn, Rivka R. Colen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/0f699f411a7a415ba931b96a4f10beda
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spelling	oai:doaj.org-article:0f699f411a7a415ba931b96a4f10beda2021-12-02T17:01:59ZMulticenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma10.1038/s41467-019-11007-02041-1723https://doaj.org/article/0f699f411a7a415ba931b96a4f10beda2019-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-11007-0https://doaj.org/toc/2041-1723MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.Nabil ElshafeeyAikaterini KotrotsouAhmed HassanNancy ElshafeiIslam HassanSara AhmedSrishti AbrolAnand AgarwalKamel El SalekSamuel BergamaschiJay AcharyaFanny E. MoronMeng LawGregory N. FullerJason T. HusePascal O. ZinnRivka R. ColenNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Nabil Elshafeey Aikaterini Kotrotsou Ahmed Hassan Nancy Elshafei Islam Hassan Sara Ahmed Srishti Abrol Anand Agarwal Kamel El Salek Samuel Bergamaschi Jay Acharya Fanny E. Moron Meng Law Gregory N. Fuller Jason T. Huse Pascal O. Zinn Rivka R. Colen Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
description	MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.
format	article
author	Nabil Elshafeey Aikaterini Kotrotsou Ahmed Hassan Nancy Elshafei Islam Hassan Sara Ahmed Srishti Abrol Anand Agarwal Kamel El Salek Samuel Bergamaschi Jay Acharya Fanny E. Moron Meng Law Gregory N. Fuller Jason T. Huse Pascal O. Zinn Rivka R. Colen
author_facet	Nabil Elshafeey Aikaterini Kotrotsou Ahmed Hassan Nancy Elshafei Islam Hassan Sara Ahmed Srishti Abrol Anand Agarwal Kamel El Salek Samuel Bergamaschi Jay Acharya Fanny E. Moron Meng Law Gregory N. Fuller Jason T. Huse Pascal O. Zinn Rivka R. Colen
author_sort	Nabil Elshafeey
title	Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
title_short	Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
title_full	Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
title_fullStr	Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
title_full_unstemmed	Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
title_sort	multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/0f699f411a7a415ba931b96a4f10beda
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Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

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