Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Jan 16, 2019

Abstract

BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development.

Authors

  • Ashirbani Saha
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. ashirbani.saha@duke.edu.
  • Lars J Grimm
  • Sujata V Ghate
    Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
  • Connie E Kim
    Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
  • Mary S Soo
    Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
  • Sora C Yoon
    Duke University Hospital, Department of Radiology, Durham, NC, USA.
  • Maciej A Mazurowski
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.

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