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AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis

Larsen, Marthe (författare)
Cancer Registry of Norway, Institute of Population-Based Cancer Research
Olstad, Camilla F. (författare)
Cancer Registry of Norway, Institute of Population-Based Cancer Research
Koch, Henrik W. (författare)
Cancer Registry of Norway, Institute of Population-Based Cancer Research
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Martiniussen, Marit A. (författare)
Østfold Hospital
Hoff, Solveig R. (författare)
Ålesund Hospital
Lund-Hanssen, Håkon (författare)
St. Olav’s University Hospital
Solli, Helene S. (författare)
Sørlandet Hospital
Mikalsen, Karl Øyvind (författare)
University Hospital of North Norway
Auensen, Steinar (författare)
UiT The Arctic University of Norway, Tromsø
Nygård, Jan (författare)
Cancer Registry of Norway, Institute of Population-Based Cancer Research
Lång, Kristina (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital,Cancer Registry of Norway, Institute of Population-Based Cancer Research
Chen, Yan (författare)
Nottingham City Hospital
Hofvind, Solveig (författare)
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 (creator_code:org_t)
2023
2023
Engelska.
Ingår i: Radiology. - 1527-1315. ; 309:1, s. 1-8
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

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