Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases

Henrik Wethe Koch; Marthe Larsen; Hauke Bartsch; Kathinka Dæhli Kurz; Solveig Hofvind

doi:10.1007/s00330-023-09461-y

Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases

Eur Radiol. 2023 May;33(5):3735-3743. doi: 10.1007/s00330-023-09461-y. Epub 2023 Mar 14.

Authors

Henrik Wethe Koch^{1

2}, Marthe Larsen³, Hauke Bartsch⁴, Kathinka Dæhli Kurz^{1

5}, Solveig Hofvind^{6

7}

Affiliations

¹ Department of Radiology, Stavanger University Hospital, Stavanger, Norway.
² Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.
³ Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
⁴ Department of Radiology, Haukeland University Hospital, Bergen, Norway.
⁵ Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, The University of Stavanger, Stavanger, Norway.
⁶ Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway. sshh@kreftregisteret.no.
⁷ Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway. sshh@kreftregisteret.no.

Abstract

Objectives: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists.

Methods: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density.

Results: A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round.

Conclusions: The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading.

Key points: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.

Keywords: Artificial intelligence; Breast neoplasm; Mammographic density; Mammography; Mass screening.

MeSH terms

Artificial Intelligence
Breast Density
Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / epidemiology
Early Detection of Cancer / methods
Female
Humans
Mammography / methods
Mass Screening / methods
Retrospective Studies