Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model.
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ICR Authors
Authors
Clendenen, TV
Ge, W
Koenig, KL
Afanasyeva, Y
Agnoli, C
Brinton, LA
Darvishian, F
Dorgan, JF
Eliassen, AH
Falk, RT
Hallmans, G
Hankinson, SE
Hoffman-Bolton, J
Key, TJ
Krogh, V
Nichols, HB
Sandler, DP
Schoemaker, MJ
Sluss, PM
Sund, M
Swerdlow, AJ
Visvanathan, K
Zeleniuch-Jacquotte, A
Liu, M
Ge, W
Koenig, KL
Afanasyeva, Y
Agnoli, C
Brinton, LA
Darvishian, F
Dorgan, JF
Eliassen, AH
Falk, RT
Hallmans, G
Hankinson, SE
Hoffman-Bolton, J
Key, TJ
Krogh, V
Nichols, HB
Sandler, DP
Schoemaker, MJ
Sluss, PM
Sund, M
Swerdlow, AJ
Visvanathan, K
Zeleniuch-Jacquotte, A
Liu, M
Document Type
Journal Article
Date
2019-03-19
Date Accepted
2019-03-05
Date Available
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.
Citation
Breast cancer research : BCR, 2019, 21 (1), pp. 42 - ?
Source Title
Publisher
BMC
ISSN
1465-5411
eISSN
1465-542X
Research Team
Aetiological Epidemiology