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dc.contributor.authorClendenen, TVen_US
dc.contributor.authorGe, Wen_US
dc.contributor.authorKoenig, KLen_US
dc.contributor.authorAfanasyeva, Yen_US
dc.contributor.authorAgnoli, Cen_US
dc.contributor.authorBrinton, LAen_US
dc.contributor.authorDarvishian, Fen_US
dc.contributor.authorDorgan, JFen_US
dc.contributor.authorEliassen, AHen_US
dc.contributor.authorFalk, RTen_US
dc.contributor.authorHallmans, Gen_US
dc.contributor.authorHankinson, SEen_US
dc.contributor.authorHoffman-Bolton, Jen_US
dc.contributor.authorKey, TJen_US
dc.contributor.authorKrogh, Ven_US
dc.contributor.authorNichols, HBen_US
dc.contributor.authorSandler, DPen_US
dc.contributor.authorSchoemaker, MJen_US
dc.contributor.authorSluss, PMen_US
dc.contributor.authorSund, Men_US
dc.contributor.authorSwerdlow, AJen_US
dc.contributor.authorVisvanathan, Ken_US
dc.contributor.authorZeleniuch-Jacquotte, Aen_US
dc.contributor.authorLiu, Men_US
dc.date.accessioned2019-04-10T09:46:15Z
dc.date.issued2019-03-19
dc.identifier.citationBreast cancer research : BCR, 2019, 21 (1), pp. 42 - ?
dc.identifier.issn1465-5411
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3172
dc.identifier.eissn1465-542X
dc.identifier.doi10.1186/s13058-019-1126-z
dc.description.abstractBACKGROUND:Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50. METHODS:In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. RESULTS:The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. CONCLUSIONS:AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.
dc.formatElectronic
dc.format.extent42 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectAnimals
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectDisease Susceptibility
dc.subjectTestosterone
dc.subjectGonadal Steroid Hormones
dc.subjectArea Under Curve
dc.subjectDiscriminant Analysis
dc.subjectRisk Assessment
dc.subjectRisk Factors
dc.subjectCase-Control Studies
dc.subjectReproducibility of Results
dc.subjectROC Curve
dc.subjectAge Factors
dc.subjectModels, Theoretical
dc.subjectAdult
dc.subjectMiddle Aged
dc.subjectFemale
dc.titleBreast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model.
dc.typeJournal Article
dcterms.dateAccepted2019-03-05
rioxxterms.versionofrecord10.1186/s13058-019-1126-z
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2019-03-19
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBreast cancer research : BCR
pubs.issue1
pubs.notesNo embargo
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/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Aetiological Epidemiology
pubs.publication-statusPublished
pubs.volume21
pubs.embargo.termsNo embargo
icr.researchteamAetiological Epidemiologyen_US
dc.contributor.icrauthorSchoemaker, Minouken


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