Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound.

Journal: Journal of breast imaging
PMID: 38243859

Abstract

OBJECTIVE: To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US.

Authors

  • Kristen Coffey
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Brianna Aukland
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Tali Amir
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Varadan Sevilimedu
    From the Department of Radiology (R.K.G.D., P.I.C.A., M.T., N.G., K.J., H.H.), Human Pathology and Pathogenesis Program, Center for Molecular Oncology (A.L.), Department of Strategy and Innovation (H.N., P.R., L.G., K.N.), and Biostatistics Service, Department of Epidemiology and Biostatistics (C.J.F., N.S., V.S.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; and School of Computing, Queens University, Kingston, Canada (K.L., K.B., F.Z., A.S.).
  • Nicole B Saphier
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Victoria L Mango
    Memorial Sloan Kettering Cancer Center, Breast and Imaging Center, 300 E 66th St, Ste 715, New York, NY 10065.

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