dc.contributor.author | Clendenen, TV | |
dc.contributor.author | Ge, W | |
dc.contributor.author | Koenig, KL | |
dc.contributor.author | Afanasyeva, Y | |
dc.contributor.author | Agnoli, C | |
dc.contributor.author | Brinton, LA | |
dc.contributor.author | Darvishian, F | |
dc.contributor.author | Dorgan, JF | |
dc.contributor.author | Eliassen, AH | |
dc.contributor.author | Falk, RT | |
dc.contributor.author | Hallmans, G | |
dc.contributor.author | Hankinson, SE | |
dc.contributor.author | Hoffman-Bolton, J | |
dc.contributor.author | Key, TJ | |
dc.contributor.author | Krogh, V | |
dc.contributor.author | Nichols, HB | |
dc.contributor.author | Sandler, DP | |
dc.contributor.author | Schoemaker, MJ | |
dc.contributor.author | Sluss, PM | |
dc.contributor.author | Sund, M | |
dc.contributor.author | Swerdlow, AJ | |
dc.contributor.author | Visvanathan, K | |
dc.contributor.author | Zeleniuch-Jacquotte, A | |
dc.contributor.author | Liu, M | |
dc.date.accessioned | 2019-04-10T09:46:15Z | |
dc.date.issued | 2019-03-19 | |
dc.identifier.citation | Breast cancer research : BCR, 2019, 21 (1), pp. 42 - ? | |
dc.identifier.issn | 1465-5411 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3172 | |
dc.identifier.eissn | 1465-542X | |
dc.identifier.doi | 10.1186/s13058-019-1126-z | |
dc.description.abstract | BACKGROUND: 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.format | Electronic | |
dc.format.extent | 42 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | BMC | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Animals | |
dc.subject | Humans | |
dc.subject | Breast Neoplasms | |
dc.subject | Disease Susceptibility | |
dc.subject | Testosterone | |
dc.subject | Gonadal Steroid Hormones | |
dc.subject | Area Under Curve | |
dc.subject | Discriminant Analysis | |
dc.subject | Risk Assessment | |
dc.subject | Risk Factors | |
dc.subject | Case-Control Studies | |
dc.subject | Reproducibility of Results | |
dc.subject | ROC Curve | |
dc.subject | Age Factors | |
dc.subject | Models, Theoretical | |
dc.subject | Adult | |
dc.subject | Middle Aged | |
dc.subject | Female | |
dc.title | Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2019-03-05 | |
rioxxterms.versionofrecord | 10.1186/s13058-019-1126-z | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-03-19 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Breast cancer research : BCR | |
pubs.issue | 1 | |
pubs.notes | No 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-status | Published | |
pubs.volume | 21 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Aetiological Epidemiology | |
dc.contributor.icrauthor | Schoemaker, Minouk | |