Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study.

Journal: The Lancet. Digital health
PMID: 39904652

Abstract

BACKGROUND: Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed.

Authors

  • Veronica Hernström
    Diagnostic Radiology, Translational Medicine, Lund University, Lund, Sweden; Radiology Department, Skåne University Hospital, Malmö, Sweden.
  • Viktoria Josefsson
    Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden.
  • Hanna Sartor
    Diagnostic Radiology, Department of Translational Medicine, Lund University, Skåne University Hospital, Lund, Sweden.
  • David Schmidt
    Skåne University Hospital, Jan Waldenströms gata 35, 205 02 Malmö, Sweden. Electronic address: david.schmidt@med.lu.se.
  • Anna-Maria Larsson
    Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
  • Solveig Hofvind
    Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
  • Ingvar Andersson
    Unilabs Breast Center, Skåne University Hospital, Jan Waldenströms gata 22, SE-20502, Malmö, Sweden.
  • Aldana Rosso
    Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden.
  • Oskar Hagberg
    Institution of Translational Medicine, Lund University, Malmö, Sweden.
  • Kristina Lång
    Institute for Biomedical Engineering, ETH Zurich, Gloriastrasse 35, 8092, Zürich, Switzerland.

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