Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Journal: Radiology
Published Date: May 23, 2023

Abstract

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, = .002), but not for historic cohort studies (0.89 vs 0.96, = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 See also the editorial by Scaranelo in this issue.

Authors

  • Jung Hyun Yoon
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea (G.R.K., E.-K.K., J.H.Y., H.J.M., J.Y.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea (S.J.K.); Department of Radiology, Ajou University School of Medicine, Suwon, Korea (E.J.H.); Yonsei University College of Medicine, Seoul, Korea (J.Y.); and Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea (H.S.L., J.H.H.).
  • Fredrik Strand
    Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden.
  • Pascal A T Baltzer
    Universitätsklinik für Radiologie und Nuklearmedizin, allgemeines Krankenhaus der Medizinischen Universität Wien, Währinger Gürtel 18-20, 1090, Wien, Österreich. pascal.baltzer@meduniwien.ac.at.
  • 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.).
  • Fiona J Gilbert
    Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; NIHR Cambridge Biomedical Research Center, Cambridge, United Kingdom.
  • Constance D Lehman
    From the Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, 55 Fruit St, WAC 240, Boston, MA 02114 (M.B., C.D.L.); and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (R.B., A.B.Y., N.J.L., L.Y.).
  • Elizabeth A Morris
    Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Lisa A Mullen
  • Robert M Nishikawa
    Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: nishikawarm@upmc.edu.
  • Nisha Sharma
    Leeds Teaching Hospital NHS Trust, Department of Radiology, Leeds, UK.
  • Ilse Vejborg
    From the Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University, College of Medicine, 50 Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass (C.D.L.); Department of Radiology, University of California Davis, Davis, Calif (E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen, Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.).
  • Linda Moy
    1 Department of Radiology, New York University School of Medicine, 160 E 34th St, New York, NY 10016.
  • Ritse M Mann
    Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.

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