The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Sophia Zackrisson, MD, PhD. Photo.

Sophia Zackrisson

Manager

Sophia Zackrisson, MD, PhD. Photo.

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

Author

  • Alejandro Rodriguez-Ruiz
  • Kristina Lång
  • Albert Gubern-Merida
  • Mireille Broeders
  • Gisella Gennaro
  • Paola Clauser
  • Thomas H Helbich
  • Margarita Chevalier
  • Tao Tan
  • Thomas Mertelmeier
  • Matthew G Wallis
  • Ingvar Andersson
  • Sophia Zackrisson
  • Ritse M Mann
  • Ioannis Sechopoulos
Show all

Summary, in English

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.

METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.

RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.

CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

Department/s

  • Radiology Diagnostics, Malmö
  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation

Publishing year

2019-03-05

Language

English

Pages

916-922

Publication/Series

Journal of the National Cancer Institute

Volume

111

Issue

9

Document type

Journal article

Publisher

Oxford University Press

Topic

  • Biomedical Laboratory Science/Technology

Status

Published

Research group

  • Radiology Diagnostics, Malmö

ISBN/ISSN/Other

  • ISSN: 1460-2105