Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 29044896

Abstract

BACKGROUND: Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

Authors

  • Igor Vidić
    Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
  • Liv Egnell
    Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
  • Neil P Jerome
    Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway.
  • Jose R Teruel
    Department of Radiology, University of California San Diego, La Jolla, California, USA.
  • Torill E Sjøbakk
    Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
  • Agnes Østlie
    Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway.
  • Hans E Fjøsne
    Department of Cancer Research and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
  • Tone F Bathen
    Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
  • Pål Erik Goa
    Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.

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