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@ARTICLE{Sasamoto:148282,
      author       = {N. Sasamoto and A. Babic and B. A. Rosner and R. T.
                      Fortner$^*$ and A. F. Vitonis and H. Yamamoto and R. N.
                      Fichorova and L. J. Titus and A. Tjønneland and L. Hansen
                      and M. Kvaskoff and A. Fournier and F. R. Mancini and H.
                      Boeing and A. Trichopoulou and E. Peppa and A. Karakatsani
                      and D. Palli and S. Grioni and A. Mattiello and R. Tumino
                      and V. Fiano and N. C. Onland-Moret and E. Weiderpass and I.
                      T. Gram and J. R. Quirós and L. Lujan-Barroso and M.-J.
                      Sánchez and S. Colorado-Yohar and A. Barricarte and P.
                      Amiano and A. Idahl and E. Lundin and H. Sartor and K.-T.
                      Khaw and T. J. Key and D. Muller and E. Riboli and M. Gunter
                      and L. Dossus and B. Trabert and N. Wentzensen and R.
                      Kaaks$^*$ and D. W. Cramer and S. S. Tworoger and K. L.
                      Terry},
      title        = {{D}evelopment and validation of circulating {CA}125
                      prediction models in postmenopausal women.},
      journal      = {Journal of ovarian research},
      volume       = {12},
      number       = {1},
      issn         = {1757-2215},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2019-02848},
      pages        = {116},
      year         = {2019},
      abstract     = {Cancer Antigen 125 (CA125) is currently the best available
                      ovarian cancer screening biomarker. However, CA125 has been
                      limited by low sensitivity and specificity in part due to
                      normal variation between individuals. Personal
                      characteristics that influence CA125 could be used to
                      improve its performance as screening biomarker.We developed
                      and validated linear and dichotomous (≥35 U/mL)
                      circulating CA125 prediction models in postmenopausal women
                      without ovarian cancer who participated in one of five large
                      population-based studies: Prostate, Lung, Colorectal, and
                      Ovarian Cancer Screening Trial (PLCO, n = 26,981),
                      European Prospective Investigation into Cancer and Nutrition
                      (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII,
                      n = 81), and the New England Case Control Study (NEC,
                      n = 923). The prediction models were developed using
                      stepwise regression in PLCO and validated in EPIC, NHS/NHSII
                      and NEC.The linear CA125 prediction model, which included
                      age, race, body mass index (BMI), smoking status and
                      duration, parity, hysterectomy, age at menopause, and
                      duration of hormone therapy (HT), explained $5\%$ of the
                      total variance of CA125. The correlation between measured
                      and predicted CA125 was comparable in PLCO testing dataset
                      (r = 0.18) and external validation datasets
                      (r = 0.14). The dichotomous CA125 prediction model
                      included age, race, BMI, smoking status and duration,
                      hysterectomy, time since menopause, and duration of HT with
                      AUC of 0.64 in PLCO and 0.80 in validation dataset.The
                      linear prediction model explained a small portion of the
                      total variability of CA125, suggesting the need to identify
                      novel predictors of CA125. The dichotomous prediction model
                      showed moderate discriminatory performance which validated
                      well in independent dataset. Our dichotomous model could be
                      valuable in identifying healthy women who may have elevated
                      CA125 levels, which may contribute to reducing false
                      positive tests using CA125 as screening biomarker.},
      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:31771659},
      pmc          = {pmc:PMC6878636},
      doi          = {10.1186/s13048-019-0591-4},
      url          = {https://inrepo02.dkfz.de/record/148282},
}