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000154432 1001_ $$0P:(DE-He78)6519c85d61a3def7974665471b8a4f74$$aHüsing, Anika$$b0$$eFirst author$$udkfz
000154432 245__ $$aValidation of two US breast cancer risk prediction models in German women.
000154432 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V.$$c2020
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000154432 520__ $$aThere are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman's age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman's medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use).We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC).Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, EBCRmod/O = 1.08 (95% confidence interval: 0.95-1.21), and EBCRAT/O = 0.99(0.87-1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat overestimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUCBCRmod = 0.61(0.58-0.63) and AUCBCRAT = 0.58(0.56-0.61), with similar values for 5-year follow-up.US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.
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000154432 7001_ $$aQuante, Anne S$$b1
000154432 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b2$$udkfz
000154432 7001_ $$aAleksandrova, Krasimira$$b3
000154432 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b4$$udkfz
000154432 7001_ $$00000-0001-7791-2698$$aPfeiffer, Ruth M$$b5
000154432 773__ $$0PERI:(DE-600)1496544-6$$a10.1007/s10552-020-01272-6$$n6$$p525-536$$tCancer causes & control$$v31$$x1573-7225$$y2020
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