Prediction of mammographic breast density based on clinical breast ultrasound images using deep learning: a retrospective analysis.

Journal: Lancet regional health. Americas
Published Date: Apr 18, 2025

Abstract

BACKGROUND: Breast density, as derived from mammographic images and defined by the Breast Imaging Reporting & Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound is an alternative breast cancer screening modality, particularly useful in low-resource, rural contexts. To date, breast ultrasound has not been used to inform risk models that need breast density. The purpose of this study is to explore the use of artificial intelligence (AI) to predict BI-RADS breast density category from clinical breast ultrasound imaging.

Authors

  • Arianna Bunnell
    Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, HI, United States of America.
  • Dustin Valdez
    University of Hawaii at Manoa, Honolulu, HI, USA; University of Hawaii Cancer Center, Honolulu, HI, USA. Electronic address: dustinkv@hawaii.edu.
  • Thomas K Wolfgruber
    From the Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo St, Suite 522, Honolulu, HI 96813 (X.Z., T.K.W., L.L., J.A.S.); Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, Hawaii (L.L., P.S.); Department of Health Sciences Research, Mayo Clinic, Rochester, Minn (M.J., C.S., S.W., C.V.); and Departments of Medicine and Epidemiology/Biostatistics, University of California, San Francisco, San Francisco, Calif (K.K.).
  • Brandon Quon
    University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
  • Kailee Hung
    Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, HI, United States of America.
  • Brenda Y Hernandez
    University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
  • Todd B Seto
    The Queen's Medical Center, 1301 Punchbowl Street, Honolulu, HI, 96813, USA.
  • Jeffrey Killeen
    Hawai'i Pacific Health, 55 Merchant St., Honolulu, HI, 96813, USA.
  • Marshall Miyoshi
    Hawai'i Diagnostic Radiology Services (St. Francis), 2230 Liliha Street, Suite 106, Honolulu, HI, 96817, USA.
  • Peter Sadowski
    Department of Computer Science, University of California, Irvine, Irvine, CA 92697-3435, USA. Electronic address: psadowsk@uci.edu.
  • John A Shepherd
    Department of Epidemiology and Population Science, University of Hawaii Cancer Center, Honolulu, HI, USA.

Keywords

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