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@ARTICLE{Li:142079,
      author       = {K. Li and G. Anderson and V. Viallon and P. Arveux and M.
                      Kvaskoff and A. Fournier and V. Krogh and R. Tumino and
                      M.-J. Sánchez and E. Ardanaz and M.-D. Chirlaque and A.
                      Agudo and D. C. Muller and T. Smith and I. Tzoulaki and T.
                      J. Key and B. Bueno-de-Mesquita and A. Trichopoulou and C.
                      Bamia and P. Orfanos and R. Kaaks$^*$ and A. Hüsing$^*$ and
                      R. Turzanski-Fortner$^*$ and A. Zeleniuch-Jacquotte and M.
                      Sund and C. C. Dahm and K. Overvad and D. Aune and E.
                      Weiderpass and I. Romieu and E. Riboli and M. J. Gunter and
                      L. Dossus and R. Prentice and P. Ferrari},
      title        = {{R}isk prediction for estrogen receptor-specific breast
                      cancers in two large prospective cohorts.},
      journal      = {Breast cancer research},
      volume       = {20},
      number       = {1},
      issn         = {1465-542X},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2018-02309},
      pages        = {147},
      year         = {2018},
      abstract     = {Few 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.},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30509329},
      pmc          = {pmc:PMC6276150},
      doi          = {10.1186/s13058-018-1073-0},
      url          = {https://inrepo02.dkfz.de/record/142079},
}