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Portrait of Sophia Zackrisson. Photo

Sophia Zackrisson

Research group manager, Principal investigator, Professor, MD

Portrait of Sophia Zackrisson. Photo

Identifying normal mammograms in a large screening population using artificial intelligence

Author

  • Kristina Lång
  • Magnus Dustler
  • Victor Dahlblom
  • Anna Åkesson
  • Ingvar Andersson
  • Sophia Zackrisson

Summary, in English

Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3–19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1–8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0–54.0) exams, including 7 (10.3%; 95% CI 3.1–17.5) cancers and 52 (27.8%; 95% CI 21.4–34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.

Department/s

  • LUCC: Lund University Cancer Centre
  • Radiology Diagnostics, Malmö
  • EpiHealth: Epidemiology for Health

Publishing year

2020

Language

English

Publication/Series

European Radiology

Document type

Journal article

Publisher

Springer

Topic

  • Cancer and Oncology

Keywords

  • Artificial intelligence
  • Breast cancer
  • Mammography
  • Mass screening

Status

Published

Project

  • Can breast cancer screening be improved with artificial intelligence?

Research group

  • Radiology Diagnostics, Malmö

ISBN/ISSN/Other

  • ISSN: 0938-7994