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dc.contributor.authorPal Choudhury, P
dc.contributor.authorWilcox, AN
dc.contributor.authorBrook, MN
dc.contributor.authorZhang, Y
dc.contributor.authorAhearn, T
dc.contributor.authorOrr, N
dc.contributor.authorCoulson, P
dc.contributor.authorSchoemaker, MJ
dc.contributor.authorJones, ME
dc.contributor.authorGail, MH
dc.contributor.authorSwerdlow, AJ
dc.contributor.authorChatterjee, N
dc.contributor.authorGarcia-Closas, M
dc.date.accessioned2019-11-15T10:58:29Z
dc.date.issued2020-03
dc.identifier.citationJournal of the National Cancer Institute, 2020, 112 (3), pp. 278 - 285
dc.identifier.issn0027-8874
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3416
dc.identifier.eissn1460-2105
dc.identifier.doi10.1093/jnci/djz113
dc.description.abstractBackground External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.Methods Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).Results The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.Conclusions iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
dc.formatPrint
dc.format.extent278 - 285
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectModels, Statistical
dc.subjectRisk
dc.subjectReproducibility of Results
dc.subjectAdolescent
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectMiddle Aged
dc.subjectFemale
dc.subjectYoung Adult
dc.titleComparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification.
dc.typeJournal Article
dcterms.dateAccepted2019-05-29
rioxxterms.versionofrecord10.1093/jnci/djz113
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/under-embargo-all-rights-reserved
rioxxterms.licenseref.startdate2020-03
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfJournal of the National Cancer Institute
pubs.issue3
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Complex Trait Genetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Oncogenetics
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Complex Trait Genetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Aetiological Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Oncogenetics
pubs.publication-statusPublished
pubs.volume112
pubs.embargo.termsNot known
icr.researchteamComplex Trait Geneticsen_US
icr.researchteamAetiological Epidemiologyen_US
icr.researchteamOncogeneticsen_US
dc.contributor.icrauthorOrr, Nicholasen
dc.contributor.icrauthorSchoemaker, Minouken
dc.contributor.icrauthorBrook, Marken
dc.contributor.icrauthorJones, Michaelen
dc.contributor.icrauthorSwerdlow, Anthonyen


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