Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

Journal: AJNR. American journal of neuroradiology
Published Date: Jun 1, 2017

Abstract

BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma.

Authors

  • P Alcaide-Leon
    From the Departments of Medical Imaging (P.A.-L., A.B.) paulaalcaideleon@hotmail.com.
  • P Dufort
    Department of Medical Imaging (P.D., A.F.G.) Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
  • A F Geraldo
    Department of Medical Imaging (P.D., A.F.G.) Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
  • L Alshafai
    Department of Medical Imaging (L.A.), Mount Sinai Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
  • P J Maralani
    Department of Medical Imaging (P.J.M.), Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada.
  • J Spears
    Neurosurgery (J.S.), St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
  • A Bharatha
    From the Departments of Medical Imaging (P.A.-L., A.B.).

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