Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

Journal: JCO clinical cancer informatics
Published Date: Feb 1, 2019

Abstract

PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and validate a deep learning model using image convolution to automatically categorize BPU on MBI.

Authors

  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Jennifer R Geske
    Mayo Clinic, Rochester MN.
  • Amy Lynn Conners
    Mayo Clinic, Rochester MN.
  • Dana H Whaley
    Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States of America.
  • Katie N Hunt
    Mayo Clinic, Rochester MN.
  • Michael K O'Connor
    Mayo Clinic, Rochester MN.
  • Deborah J Rhodes
    Mayo Clinic, Rochester MN.
  • Carrie B Hruska
    Mayo Clinic, Rochester MN.

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