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

Journal: Academic radiology
Published Date: Feb 20, 2020

Abstract

RATIONALE AND OBJECTIVES: Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clinically implemented our deep learning model at the academic breast imaging practice where the model was developed with high clinical acceptance. Our objective was to externally validate our deep learning model on radiologist breast density assessments in a community breast imaging practice.

Authors

  • Brian N Dontchos
  • Adam Yala
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Justin Xiang
    Massachusetts Institute of Technology, Cambridge, 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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