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@ARTICLE{Feng:292455,
      author       = {X. Feng and P. Goodley and K. Alcala and F. Guida and R.
                      Kaaks$^*$ and R. Vermeulen and G. S. Downward and C. Bonet
                      and S. M. Colorado-Yohar and D. Albanes and S. J. Weinstein
                      and M. Goldberg and M. Zins and C. Relton and A. Langhammer
                      and A. H. Skogholt and M. Johansson and H. A. Robbins},
      title        = {{E}valuation of risk prediction models to select lung
                      cancer screening participants in {E}urope: a prospective
                      cohort consortium analysis.},
      journal      = {The lancet / Digital health},
      volume       = {6},
      number       = {9},
      issn         = {2589-7500},
      address      = {London},
      publisher    = {The Lancet},
      reportid     = {DKFZ-2024-01722},
      pages        = {e614 - e624},
      year         = {2024},
      abstract     = {Lung cancer risk prediction models might efficiently
                      identify individuals who should be offered lung cancer
                      screening. However, their performance has not been
                      comprehensively evaluated in Europe. We aimed to externally
                      validate and evaluate the performance of several risk
                      prediction models that predict lung cancer incidence or
                      mortality in prospective European cohorts.We analysed 240
                      137 participants aged 45-80 years with a current or former
                      smoking history from nine European countries in four
                      prospective cohorts from the pooled database of the Lung
                      Cancer Cohort Consortium: the Alpha-Tocopherol,
                      Beta-Carotene Cancer Prevention Study (Finland), the
                      Nord-Trøndelag Health Study (Norway), CONSTANCES (France),
                      and the European Prospective Investigation into Cancer and
                      Nutrition (Denmark, Germany, Italy, Spain, Sweden, the
                      Netherlands, and Norway). We evaluated ten lung cancer risk
                      models, which comprised the Bach, the Prostate, Lung,
                      Colorectal, and Ovarian Cancer Screening Trial 2012 model
                      (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT),
                      the Lung Cancer Death Risk Assessment Tool (LCDRAT), the
                      Nord-Trøndelag Health Study (HUNT), the Optimized Early
                      Warning Model for Lung Cancer Risk (OWL), the University
                      College London-Death (UCLD), the University College
                      London-Incidence (UCLI), the Liverpool Lung Project version
                      2 (LLP version 2), and the Liverpool Lung Project version 3
                      (LLP version 3) models. We quantified model calibration as
                      the ratio of expected to observed cases or deaths and
                      discrimination using the area under the receiver operating
                      characteristic curve (AUC). For each model, we also
                      identified risk thresholds that would screen the same number
                      of individuals as each of the US Preventive Services Task
                      Force 2021 (USPSTF-2021), the US Preventive Services Task
                      Force 2013 (USPSTF-2013), and the Nederlands-Leuvens
                      Longkanker Screenings Onderzoek (NELSON) criteria.Among the
                      participants, 1734 lung cancer cases and 1072 lung cancer
                      deaths occurred within five years of enrolment. Most models
                      had reasonable calibration in most countries, although the
                      LLP version 2 overpredicted risk by more than $50\%$ in
                      eight countries (expected to observed ≥1·50). The
                      PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI
                      models showed similar discrimination in most countries, with
                      AUCs ranging from 0·68 $(95\%$ CI 0·59-0·77) to 0·83
                      (0·78-0·89), whereas the LLP version 2 and LLP version 3
                      showed lower discrimination, with AUCs ranging from 0·64
                      $(95\%$ CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling
                      data from all countries (but excluding the HUNT cohort),
                      $33·9\%$ (73 313 of 216 387) of individuals were eligible
                      by USPSTF-2021 criteria, which included $74·8\%$ (1185) of
                      lung cancers and $76·3\%$ (730) of lung cancer deaths
                      occurring over 5 years. Fewer individuals were selected by
                      USPSTF-2013 and NELSON criteria. After applying thresholds
                      to select a population of equal size to USPSTF-2021, the
                      PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI,
                      models identified $77·6\%-79·1\%$ of future cases,
                      although they selected slightly older individuals compared
                      with USPSTF-2021 criteria. Results were similar for
                      USPSTF-2013 and NELSON.Several lung cancer risk prediction
                      models showed good performance in European countries and
                      might improve the efficiency of lung cancer screening if
                      used in place of categorical eligibility criteria.US
                      National Cancer Institute, l'Institut National du Cancer,
                      Cancer Research UK.},
      keywords     = {Humans / Lung Neoplasms: diagnosis / Lung Neoplasms:
                      mortality / Europe: epidemiology / Aged / Male / Female /
                      Middle Aged / Prospective Studies / Early Detection of
                      Cancer / Risk Assessment / Aged, 80 and over / Incidence /
                      Risk Factors},
      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:39179310},
      doi          = {10.1016/S2589-7500(24)00123-7},
      url          = {https://inrepo02.dkfz.de/record/292455},
}