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@ARTICLE{Brantley:300748,
      author       = {K. D. Brantley and M. E. Jones and R. M. Tamimi and B. A.
                      Rosner and P. Kraft and H. B. Nichols and K. M. O'Brien and
                      H.-O. Adami and A. Aizpurua and A. B. de Gonzalez and W. J.
                      Blot and T. Braaten and Y. Chen and J. C. DeHart and L.
                      Dossus and S. Elias and R. Turzanski-Fortner$^*$ and M.
                      Garcia-Closas and I. T. Gram and N. Håkansson and S. E.
                      Hankinson and C. M. Kitahara and W.-P. Koh and M. S. Linet
                      and R. J. MacInnis and G. Masala and L. Mellemkjær and R.
                      L. Milne and D. C. Muller and H. L. Park and K. J. Ruddy and
                      S. Sandin and X.-O. Shu and S. Tin Tin and T. Truong and C.
                      M. Vachon and L. J. Vatten and K. Visvanathan and E.
                      Weiderpass and W. Willett and A. Wolk and J.-M. Yuan and W.
                      Zheng and D. P. Sandler and M. J. Schoemaker and A. J.
                      Swerdlow and A. H. Eliassen},
      title        = {{D}evelopment and validation of a risk prediction model for
                      premenopausal breast cancer in 19 cohorts.},
      journal      = {Breast cancer research},
      volume       = {27},
      number       = {1},
      issn         = {1465-5411},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-00908},
      pages        = {67},
      year         = {2025},
      abstract     = {Incidence of premenopausal breast cancer (BC) has risen in
                      recent years, though most existing BC prediction models are
                      not generalizable to young women due to underrepresentation
                      of this age group in model development.Using
                      questionnaire-based data from 19 prospective studies
                      harmonized within the Premenopausal Breast Cancer
                      Collaborative Group (PBCCG), representing 783,830 women, we
                      developed a premenopausal BC risk prediction model. The data
                      were split into training (2/3) and validation (1/3) datasets
                      with equal distribution of cohorts in each. In the training
                      dataset variables were chosen from known and hypothesized
                      risk factors: age, age at menarche, age at first birth,
                      parity, breastfeeding, height, BMI, young adulthood BMI,
                      recent weight change, alcohol consumption, first-degree
                      family history of BC, and personal history of benign breast
                      disease (BBD). Hazard ratios (HR) and $95\%$ confidence
                      intervals (CI) were estimated by Cox proportional hazards
                      regression using age as time scale, stratified by cohort.
                      Given that complete information on all risk factors was not
                      available in all cohorts, coefficients were estimated
                      separately in groups of cohorts with the same available
                      covariate information, adjusted to account for the
                      correlation between missing and non-missing variables and
                      meta-analyzed. Absolute risk of BC (in situ or invasive)
                      within 5 years, was determined using country-, age-, and
                      birth cohort-specific incidence rates. Discrimination (area
                      under the curve, AUC) and calibration (Expected/Observed,
                      E/O) were evaluated in the validation dataset. We compared
                      our model with a literature-based model for women < 50 years
                      (iCARE-Lit).Selected model risk factors were age at
                      menarche, parity, height, current and young adulthood BMI,
                      family history of BC, and personal BBD history. Predicted
                      absolute 5-year risk ranged from $0\%$ to $5.7\%.$ The model
                      overestimated risk on average [E/O risk = 1.18 (1.14-1.23)],
                      with underestimation of risk in lower absolute risk deciles
                      and overestimation in upper absolute risk deciles [E/O 1st
                      decile = 0.59 (0.58-0.60); E/O 10th decile = 1.48
                      (1.48-1.49)]. The AUC was $59.1\%$ $(58.1-60.1\%).$
                      Performance was similar to the iCARE-Lit model.In this
                      prediction model for premenopausal BC, the relative
                      contribution of risk factors to absolute risk was similar to
                      existing models for overall BC. The discriminatory ability
                      was nearly identical (< $1\%$ difference in AUC) to the
                      existing iCARE-Lit model developed in women under 50 years.
                      The inability to improve discrimination highlights the need
                      to investigate additional predictors to better understand
                      premenopausal BC risk.},
      keywords     = {Premenopausal breast cancer (Other) / Risk prediction model
                      (Other) / Young-onset breast cancer (Other)},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40312753},
      doi          = {10.1186/s13058-025-02031-8},
      url          = {https://inrepo02.dkfz.de/record/300748},
}