A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer.

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dc.creatorEriksson, Mikael; orcid: 0000-0001-8135-4270
dc.creatorCzene, Kamila; orcid: 0000-0002-3233-5695
dc.creatorVachon, Celine
dc.creatorConant, Emily F
dc.creatorHall, Per; orcid: 0000-0002-5640-9126
dc.date2023-08-22T13:37:26Z
dc.date2023-08-22T13:37:26Z
dc.date2023-06-19
dc.date2023-08-22T13:37:25Z
dc.date.accessioned2026-08-03T01:24:58Z
dc.descriptionBackgroundImage-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated.MethodsWe performed a case-cohort study of 8110 women aged 40-74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer-Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up.ResultsThe AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70-0.80) to 0.68 (95%CI: 0.66-0.69) 1-10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66-0.78) to 0.65 (95%CI: 0.63-0.66) for the imaging-only model and 0.62 (95%CI: 0.55-0.68) to 0.60 (95%CI: 0.58-0.61) for Tyrer-Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model, p p ConclusionsThe lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer-Cuzick models.
dc.formatapplication/pdf
dc.identifier2072-6694
dc.identifier37370856
dc.identifierPMC10296673
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/354928
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/153607
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2072-6694
dc.sourcenlmid: 101526829
dc.subjectArtificial intelligence
dc.subjectBreast cancer
dc.subjectPrimary Prevention
dc.subjectRisk Model
dc.subjectLong-term Risk
dc.subjectIndividualized Screening
dc.subjectImage-derived Risk Model
dc.titleA Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer.
dc.typeArticle

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