Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Journal: Breast cancer research : BCR
Published Date: Feb 20, 2022

Abstract

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.

Authors

  • Aimilia Gastounioti
    Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
  • Shyam Desai
    Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Vinayak S Ahluwalia
    Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Emily F Conant
    Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).
  • Despina Kontos
    Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States.

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