Nature Communications (Jul 2019)
Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
- 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
Affiliations
- Nabil Elshafeey
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Aikaterini Kotrotsou
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Ahmed Hassan
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Nancy Elshafei
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center
- Islam Hassan
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center
- Sara Ahmed
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center
- Srishti Abrol
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Anand Agarwal
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Kamel El Salek
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- Samuel Bergamaschi
- Department of Radiology, University of Southern California, Keck School of Medicine
- Jay Acharya
- Department of Radiology, University of Southern California, Keck School of Medicine
- Fanny E. Moron
- Department of Radiology, Baylor College of Medicine
- Meng Law
- Department of Radiology, University of Southern California, Keck School of Medicine
- Gregory N. Fuller
- Department of Pathology, Anatomical and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
- Jason T. Huse
- Department of Pathology, Anatomical and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
- Pascal O. Zinn
- Department of Neurosurgery, Baylor College of Medicine
- Rivka R. Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center
- DOI
- https://doi.org/10.1038/s41467-019-11007-0
- Journal volume & issue
-
Vol. 10,
no. 1
pp. 1 – 9
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.
