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

Authors :
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
Source :
Nature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
Publication Year :
2019
Publisher :
Nature Portfolio, 2019.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.0f699f411a7a415ba931b96a4f10beda
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-019-11007-0