Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal: Journal of the American College of Radiology : JACR
Published Date: Mar 1, 2018

Abstract

PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy.

Authors

  • Bibo Shi
    Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina. Electronic address: bibo.shi@duke.edu.
  • Lars J Grimm
  • Maciej A Mazurowski
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.
  • Jay A Baker
    Duke University Hospital, Department of Radiology, Durham, NC, USA.
  • Jeffrey R Marks
    Department of Surgery, Duke University School of Medicine, Durham, North Carolina.
  • Lorraine M King
    Department of Surgery, Duke University School of Medicine, Durham, North Carolina.
  • Carlo C Maley
    Biodesign Center for Personalized Diagnostics and School of Life Sciences, Arizona State University, Tempe, Arizona; Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom.
  • E Shelley Hwang
    Department of Surgery, Duke University School of Medicine, Durham, North Carolina.
  • Joseph Y Lo
    Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina.

Keywords

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