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@ARTICLE{Smith:141708,
      author       = {T. Smith and D. C. Muller and K. G. M. Moons and A. J.
                      Cross and M. Johansson and P. Ferrari and G. Fagherazzi and
                      P. H. M. Peeters and G. Severi and A. Hüsing$^*$ and R.
                      Kaaks$^*$ and A. Tjonneland and A. Olsen and K. Overvad and
                      C. Bonet and M. Rodriguez-Barranco and J. M. Huerta and A.
                      Barricarte Gurrea and K. E. Bradbury and A. Trichopoulou and
                      C. Bamia and P. Orfanos and D. Palli and V. Pala and P.
                      Vineis and B. Bueno-de-Mesquita and B. Ohlsson and S. Harlid
                      and B. Van Guelpen and G. Skeie and E. Weiderpass and M.
                      Jenab and N. Murphy and E. Riboli and M. J. Gunter and K. J.
                      Aleksandrova and I. Tzoulaki},
      title        = {{C}omparison of prognostic models to predict the occurrence
                      of colorectal cancer in asymptomatic individuals: a
                      systematic literature review and external validation in the
                      {EPIC} and {UK} {B}iobank prospective cohort studies.},
      journal      = {Gut},
      volume       = {68},
      number       = {4},
      issn         = {1468-3288},
      address      = {London},
      publisher    = {BMJ Publishing Group},
      reportid     = {DKFZ-2018-01979},
      pages        = {672-683},
      year         = {2019},
      abstract     = {To systematically identify and validate published
                      colorectal cancer risk prediction models that do not require
                      invasive testing in two large population-based prospective
                      cohorts.Models were identified through an update of a
                      published systematic review and validated in the European
                      Prospective Investigation into Cancer and Nutrition (EPIC)
                      and the UK Biobank. The performance of the models to predict
                      the occurrence of colorectal cancer within 5 or 10 years
                      after study enrolment was assessed by discrimination
                      (C-statistic) and calibration (plots of observed vs
                      predicted probability).The systematic review and its update
                      identified 16 models from 8 publications (8 colorectal, 5
                      colon and 3 rectal). The number of participants included in
                      each model validation ranged from 41 587 to 396 515, and
                      the number of cases ranged from 115 to 1781. Eligible and
                      ineligible participants across the models were largely
                      comparable. Calibration of the models, where assessable, was
                      very good and further improved by recalibration. The
                      C-statistics of the models were largely similar between
                      validation cohorts with the highest values achieved being
                      0.70 $(95\%$ CI 0.68 to 0.72) in the UK Biobank and 0.71
                      $(95\%$ CI 0.67 to 0.74) in EPIC.Several of these
                      non-invasive models exhibited good calibration and
                      discrimination within both external validation populations
                      and are therefore potentially suitable candidates for the
                      facilitation of risk stratification in population-based
                      colorectal screening programmes. Future work should both
                      evaluate this potential, through modelling and impact
                      studies, and ascertain if further enhancement in their
                      performance can be obtained.},
      subtyp        = {Review Article},
      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:29615487},
      doi          = {10.1136/gutjnl-2017-315730},
      url          = {https://inrepo02.dkfz.de/record/141708},
}