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@ARTICLE{Risk:136726,
      author       = {F. Guida and N. Sun and L. E. Bantis and D. C. Muller and
                      P. Li and A. Taguchi and D. Dhillon and D. L. Kundnani and
                      N. J. Patel and Q. Yan and G. Byrnes and K. G. M. Moons and
                      A. Tjønneland and S. Panico and C. Agnoli and P. Vineis and
                      D. Palli and B. Bueno-de-Mesquita and P. H. Peeters and A.
                      Agudo and J. M. Huerta and M. Dorronsoro and M. R. Barranco
                      and E. Ardanaz and R. C. Travis and K. S. Byrne and H.
                      Boeing and A. Steffen and R. Kaaks$^*$ and A. Hüsing$^*$
                      and A. Trichopoulou and P. Lagiou and C. La Vecchia and G.
                      Severi and M.-C. Boutron-Ruault and T. M. Sandanger and E.
                      W. Vainio and T. H. Nøst and K. Tsilidis and E. Riboli and
                      K. Grankvist and M. Johansson and G. E. Goodman and Z. Feng
                      and P. Brennan and M. Johansson and S. M. Hanash},
      collaboration = {I. A. o. L. C. E. a. Risk},
      title        = {{A}ssessment of {L}ung {C}ancer {R}isk on the {B}asis of a
                      {B}iomarker {P}anel of {C}irculating {P}roteins.},
      journal      = {JAMA oncology},
      volume       = {N.N.},
      issn         = {2374-2437},
      address      = {Chicago, Ill.},
      publisher    = {American Medical Association},
      reportid     = {DKFZ-2018-01164},
      pages        = {e182078},
      year         = {2018},
      abstract     = {There is an urgent need to improve lung cancer risk
                      assessment because current screening criteria miss a large
                      proportion of cases.To investigate whether a lung cancer
                      risk prediction model based on a panel of selected
                      circulating protein biomarkers can outperform a traditional
                      risk prediction model and current US screening
                      criteria.Prediagnostic samples from 108 ever-smoking
                      patients with lung cancer diagnosed within 1 year after
                      blood collection and samples from 216 smoking-matched
                      controls from the Carotene and Retinol Efficacy Trial
                      (CARET) cohort were used to develop a biomarker risk score
                      based on 4 proteins (cancer antigen 125 [CA125],
                      carcinoembryonic antigen [CEA], cytokeratin-19 fragment
                      [CYFRA 21-1], and the precursor form of surfactant protein B
                      [Pro-SFTPB]). The biomarker score was subsequently validated
                      blindly using absolute risk estimates among 63 ever-smoking
                      patients with lung cancer diagnosed within 1 year after
                      blood collection and 90 matched controls from 2 large
                      European population-based cohorts, the European Prospective
                      Investigation into Cancer and Nutrition (EPIC) and the
                      Northern Sweden Health and Disease Study (NSHDS).Model
                      validity in discriminating between future lung cancer cases
                      and controls. Discrimination estimates were weighted to
                      reflect the background populations of EPIC and NSHDS
                      validation studies (area under the receiver-operating
                      characteristics curve [AUC], sensitivity, and
                      specificity).In the validation study of 63 ever-smoking
                      patients with lung cancer and 90 matched controls (mean [SD]
                      age, 57.7 [8.7] years; $68.6\%$ men) from EPIC and NSHDS, an
                      integrated risk prediction model that combined smoking
                      exposure with the biomarker score yielded an AUC of 0.83
                      $(95\%$ CI, 0.76-0.90) compared with 0.73 $(95\%$ CI,
                      0.64-0.82) for a model based on smoking exposure alone
                      (P = .003 for difference in AUC). At an overall
                      specificity of 0.83, based on the US Preventive Services
                      Task Force screening criteria, the sensitivity of the
                      integrated risk prediction (biomarker) model was 0.63
                      compared with 0.43 for the smoking model. Conversely, at an
                      overall sensitivity of 0.42, based on the US Preventive
                      Services Task Force screening criteria, the integrated risk
                      prediction model yielded a specificity of 0.95 compared with
                      0.86 for the smoking model.This study provided a proof of
                      principle in showing that a panel of circulating protein
                      biomarkers may improve lung cancer risk assessment and may
                      be used to define eligibility for computed tomography
                      screening.},
      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:30003238},
      doi          = {10.1001/jamaoncol.2018.2078},
      url          = {https://inrepo02.dkfz.de/record/136726},
}