Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Journal: Journal of the National Cancer Institute
Published Date:

Abstract

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.

Authors

  • Alejandro Rodríguez-Ruiz
    From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.).
  • Kristina Lång
    Institute for Biomedical Engineering, ETH Zurich, Gloriastrasse 35, 8092, Zürich, Switzerland.
  • Albert Gubern-Mérida
    Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Mireille Broeders
    Department for Health Evidence, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Gisella Gennaro
    Veneto Institute of Oncology (IOV)-IRCCS, via Gattamelata 64, 35128, Padua, Italy.
  • Paola Clauser
    Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
  • Thomas H Helbich
    Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy.
  • Margarita Chevalier
    Medical Physics Group, Radiology Department, Faculty of Medicine, Universidad Complutense de Madrid, Pza. Ramón y Cajal s/n, 28040, Madrid, Spain.
  • Tao Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
  • Thomas Mertelmeier
    Siemens Healthcare GmbH, Diagnostic Imaging, X-Ray Products, Technology & Concepts, Siemensstr. 3, 91301, Forchheim, Germany.
  • Matthew G Wallis
    Cambridge Breast Unit and NIHR Biomedical Research Unit, Box 97, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK.
  • Ingvar Andersson
    Unilabs Breast Center, Skåne University Hospital, Jan Waldenströms gata 22, SE-20502, Malmö, Sweden.
  • Sophia Zackrisson
    Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
  • Ritse M Mann
    Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
  • Ioannis Sechopoulos
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.