Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

Journal: Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Published Date: May 16, 2018

Abstract

PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T-weighted images.

Authors

  • Akira Kunimatsu
  • Natsuko Kunimatsu
    Department of Radiology, International University of Health and Welfare, Mita Hospital.
  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Hiroyuki Akai
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Kouhei Kamiya
    Department of Radiology, The University of Tokyo.
  • Takeyuki Watadani
    Department of Radiology, Faculty of Medicine, The University of Tokyo.
  • Harushi Mori
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

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