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@ARTICLE{Aleksandrova:166661,
      author       = {K. Aleksandrova and R. Reichmann and R. Kaaks$^*$ and M.
                      Jenab and H. B. Bueno-de-Mesquita and C. C. Dahm and A. K.
                      Eriksen and A. Tjønneland and F. Artaud and M.-C.
                      Boutron-Ruault and G. Severi and A. Hüsing$^*$ and A.
                      Trichopoulou and A. Karakatsani and E. Peppa and S. Panico
                      and G. Masala and S. Grioni and C. Sacerdote and R. Tumino
                      and S. G. Elias and A. M. May and K. B. Borch and T. M.
                      Sandanger and G. Skeie and M.-J. Sánchez and J. M. Huerta
                      and N. Sala and A. B. Gurrea and J. R. Quirós and P. Amiano
                      and J. Berntsson and I. Drake and B. van Guelpen and S.
                      Harlid and T. Key and E. Weiderpass and E. K. Aglago and A.
                      J. Cross and K. K. Tsilidis and E. Riboli and M. J. Gunter},
      title        = {{D}evelopment and validation of a lifestyle-based model for
                      colorectal cancer risk prediction: the {L}i{F}e{CRC} score.},
      journal      = {BMC medicine},
      volume       = {19},
      number       = {1},
      issn         = {1741-7015},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DKFZ-2021-00028},
      pages        = {1},
      year         = {2021},
      abstract     = {Nutrition and lifestyle have been long established as risk
                      factors for colorectal cancer (CRC). Modifiable lifestyle
                      behaviours bear potential to minimize long-term CRC risk;
                      however, translation of lifestyle information into
                      individualized CRC risk assessment has not been implemented.
                      Lifestyle-based risk models may aid the identification of
                      high-risk individuals, guide referral to screening and
                      motivate behaviour change. We therefore developed and
                      validated a lifestyle-based CRC risk prediction algorithm in
                      an asymptomatic European population.The model was based on
                      data from 255,482 participants in the European Prospective
                      Investigation into Cancer and Nutrition (EPIC) study aged 19
                      to 70 years who were free of cancer at study baseline
                      (1992-2000) and were followed up to 31 September 2010. The
                      model was validated in a sample comprising 74,403
                      participants selected among five EPIC centres. Over a median
                      follow-up time of 15 years, there were 3645 and 981
                      colorectal cancer cases in the derivation and validation
                      samples, respectively. Variable selection algorithms in Cox
                      proportional hazard regression and random survival forest
                      (RSF) were used to identify the best predictors among
                      plausible predictor variables. Measures of discrimination
                      and calibration were calculated in derivation and validation
                      samples. To facilitate model communication, a nomogram and a
                      web-based application were developed.The final selection
                      model included age, waist circumference, height, smoking,
                      alcohol consumption, physical activity, vegetables, dairy
                      products, processed meat, and sugar and confectionary. The
                      risk score demonstrated good discrimination overall and in
                      sex-specific models. Harrell's C-index was 0.710 in the
                      derivation cohort and 0.714 in the validation cohort. The
                      model was well calibrated and showed strong agreement
                      between predicted and observed risk. Random survival forest
                      analysis suggested high model robustness. Beyond age,
                      lifestyle data led to improved model performance overall
                      (continuous net reclassification improvement = 0.307 $(95\%$
                      CI 0.264-0.352)), and especially for young individuals below
                      45 years (continuous net reclassification improvement =
                      0.364 $(95\%$ CI 0.084-0.575)).LiFeCRC score based on age
                      and lifestyle data accurately identifies individuals at risk
                      for incident colorectal cancer in European populations and
                      could contribute to improved prevention through motivating
                      lifestyle change at an individual level.},
      keywords     = {Cancer prevention (Other) / Colorectal cancer (Other) /
                      Lifestyle behaviour (Other) / Risk prediction (Other) / Risk
                      screening (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:33390155},
      doi          = {10.1186/s12916-020-01826-0},
      url          = {https://inrepo02.dkfz.de/record/166661},
}