Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

Journal: Journal of the American College of Radiology : JACR
Published Date: May 23, 2022

Abstract

OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice.

Authors

  • Brian N Dontchos
  • Katherine Cavallo-Hom
    Massachusetts General Hospital, Boston, Massachusetts.
  • Leslie R Lamb
    Harvard Medical School, Boston, Massachusetts; Massachusetts General Hospital, Boston, Massachusetts.
  • Sarah F Mercaldo
    Massachusetts General Hospital, Boston, Massachusetts.
  • Martin Eklund
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: martin.eklund@ki.se.
  • Pragya Dang
    Newton-Wellesley Hospital, Newton, Massachusetts.
  • Constance D Lehman
    From the Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, 55 Fruit St, WAC 240, Boston, MA 02114 (M.B., C.D.L.); and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (R.B., A.B.Y., N.J.L., L.Y.).

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