Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

Journal: Magnetic resonance imaging
Published Date: Aug 16, 2019

Abstract

BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of multi-paramedic MRI.

Authors

  • Gordian Hamerla
    Department of Neuroradiology, University of Leipzig, Leipzig, Germany. Electronic address: gordian.hamerla@medizin.uni-leipzig.de.
  • Hans-Jonas Meyer
    Department of Diagnostic and Interventional Radiology, University of Leip-zig, Leipzig, Germany.
  • Stefan Schob
    Department of Neuroradiology, University of Leipzig, Leipzig, Germany.
  • Daniel T Ginat
    University of Chicago, Pritzker School of Medicine, Chicago, IL, USA.
  • Ashley Altman
    University of Chicago, Pritzker School of Medicine, Chicago, IL, USA.
  • Tchoyoson Lim
    Neuroradiology, National Neuroscience Institute, Singapore 308433, Singapore. tchoyoson.lim@singhealth.com.sg.
  • Georg Alexander Gihr
    Clinic for Neuroradiology, Katharinenhospital Stuttgart, Stuttgart, Germany.
  • Diana Horvath-Rizea
    Clinic for Neuroradiology, Katharinenhospital Stuttgart, Stuttgart, Germany.
  • Karl-Titus Hoffmann
    Department of Neuroradiology, University of Leipzig, Leipzig, Germany.
  • Alexey Surov
    Department of Diagnostic and Interventional Radiology, University of Leip-zig, Leipzig, Germany.

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