Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Cerebral Gliomas

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Author(s)

    • Kudo Kohsuke
    • Division of Ultra-High Field MRI, Iwate Medical University|Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital
    • Uwano Ikuko
    • Division of Ultra-High Field MRI, Iwate Medical University
    • Hirai Toshinori
    • Department of Radiology, Faculty of Medicine, University of Miyazaki
    • Fujima Noriyuki
    • Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital
    • Goodwin Jonathan
    • Laboratory of Biofunctional Imaging, Immunology Frontier Research Center, Osaka University

Abstract

<p>Purpose: The purpose of the present study was to compare different software algorithms for processing DSC perfusion images of cerebral tumors with respect to i) the relative CBV (rCBV) calculated, ii) the cutoff value for discriminating low- and high-grade gliomas, and iii) the diagnostic performance for differentiating these tumors.</p><p>Methods: Following approval of institutional review board, informed consent was obtained from all patients. Thirty-five patients with primary glioma (grade II, 9; grade III, 8; and grade IV, 18 patients) were included. DSC perfusion imaging was performed with 3-Tesla MRI scanner. CBV maps were generated by using 11 different algorithms of four commercially available software and one academic program. rCBV of each tumor compared to normal white matter was calculated by ROI measurements. Differences in rCBV value were compared between algorithms for each tumor grade. Receiver operator characteristics analysis was conducted for the evaluation of diagnostic performance of different algorithms for differentiating between different grades.</p><p>Results: Several algorithms showed significant differences in rCBV, especially for grade IV tumors. When differentiating between low- (II) and high-grade (III/IV) tumors, the area under the ROC curve (Az) was similar (range 0.85–0.87), and there were no significant differences in Az between any pair of algorithms. In contrast, the optimal cutoff values varied between algorithms (range 4.18–6.53).</p><p>Conclusions: rCBV values of tumor and cutoff values for discriminating low- and high-grade gliomas differed between software packages, suggesting that optimal software-specific cutoff values should be used for diagnosis of high-grade gliomas.</p>

Journal

  • Magnetic Resonance in Medical Sciences

    Magnetic Resonance in Medical Sciences 16(2), 129-136, 2017

    Japanese Society for Magnetic Resonance in Medicine

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