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000142079 0247_ $$2doi$$a10.1186/s13058-018-1073-0
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000142079 1001_ $$aLi, Kuanrong$$b0
000142079 245__ $$aRisk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts.
000142079 260__ $$aLondon$$bBioMed Central$$c2018
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000142079 520__ $$aFew published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction.We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
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000142079 7001_ $$aAnderson, Garnet$$b1
000142079 7001_ $$aViallon, Vivian$$b2
000142079 7001_ $$aArveux, Patrick$$b3
000142079 7001_ $$aKvaskoff, Marina$$b4
000142079 7001_ $$aFournier, Agnès$$b5
000142079 7001_ $$aKrogh, Vittorio$$b6
000142079 7001_ $$aTumino, Rosario$$b7
000142079 7001_ $$aSánchez, Maria-Jose$$b8
000142079 7001_ $$aArdanaz, Eva$$b9
000142079 7001_ $$aChirlaque, María-Dolores$$b10
000142079 7001_ $$aAgudo, Antonio$$b11
000142079 7001_ $$aMuller, David C$$b12
000142079 7001_ $$aSmith, Todd$$b13
000142079 7001_ $$aTzoulaki, Ioanna$$b14
000142079 7001_ $$aKey, Timothy J$$b15
000142079 7001_ $$aBueno-de-Mesquita, Bas$$b16
000142079 7001_ $$aTrichopoulou, Antonia$$b17
000142079 7001_ $$aBamia, Christina$$b18
000142079 7001_ $$aOrfanos, Philippos$$b19
000142079 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b20$$udkfz
000142079 7001_ $$0P:(DE-He78)6519c85d61a3def7974665471b8a4f74$$aHüsing, Anika$$b21$$udkfz
000142079 7001_ $$0P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2$$aTurzanski-Fortner, Renée$$b22$$udkfz
000142079 7001_ $$aZeleniuch-Jacquotte, Anne$$b23
000142079 7001_ $$aSund, Malin$$b24
000142079 7001_ $$aDahm, Christina C$$b25
000142079 7001_ $$aOvervad, Kim$$b26
000142079 7001_ $$aAune, Dagfinn$$b27
000142079 7001_ $$aWeiderpass, Elisabete$$b28
000142079 7001_ $$aRomieu, Isabelle$$b29
000142079 7001_ $$aRiboli, Elio$$b30
000142079 7001_ $$aGunter, Marc J$$b31
000142079 7001_ $$aDossus, Laure$$b32
000142079 7001_ $$aPrentice, Ross$$b33
000142079 7001_ $$aFerrari, Pietro$$b34
000142079 773__ $$0PERI:(DE-600)2041618-0$$a10.1186/s13058-018-1073-0$$gVol. 20, no. 1, p. 147$$n1$$p147$$tBreast cancer research$$v20$$x1465-542X$$y2018
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