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 () and Rivka R. Colen ()
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Nabil Elshafeey: The University of Texas MD Anderson Cancer Center
Aikaterini Kotrotsou: The University of Texas MD Anderson Cancer Center
Ahmed Hassan: The University of Texas MD Anderson Cancer Center
Nancy Elshafei: The University of Texas MD Anderson Cancer Center
Islam Hassan: The University of Texas MD Anderson Cancer Center
Sara Ahmed: The University of Texas MD Anderson Cancer Center
Srishti Abrol: The University of Texas MD Anderson Cancer Center
Anand Agarwal: The University of Texas MD Anderson Cancer Center
Kamel El Salek: The University of Texas MD Anderson Cancer Center
Samuel Bergamaschi: University of Southern California, Keck School of Medicine
Jay Acharya: University of Southern California, Keck School of Medicine
Fanny E. Moron: Baylor College of Medicine
Meng Law: University of Southern California, Keck School of Medicine
Gregory N. Fuller: The University of Texas MD Anderson Cancer Center
Jason T. Huse: The University of Texas MD Anderson Cancer Center
Pascal O. Zinn: Baylor College of Medicine
Rivka R. Colen: The University of Texas MD Anderson Cancer Center

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

Date: 2019
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DOI: 10.1038/s41467-019-11007-0

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