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@ARTICLE{GonzalezMaldonado:168497,
      author       = {S. Gonzalez Maldonado$^*$ and L. C. Hynes$^*$ and E.
                      Motsch$^*$ and C.-P. Heussel and H.-U. Kauczor and H. A.
                      Robbins and S. Delorme$^*$ and R. Kaaks$^*$},
      title        = {{V}alidation of multivariable lung cancer risk prediction
                      models for the personalized assignment of optimal screening
                      frequency: a retrospective analysis of data from the
                      {G}erman {L}ung {C}ancer {S}creening {I}ntervention {T}rial
                      ({LUSI}).},
      journal      = {Translational Lung Cancer Research},
      volume       = {10},
      number       = {3},
      issn         = {2226-4477},
      address      = {[S.l.]},
      reportid     = {DKFZ-2021-00932},
      pages        = {1305 - 1317},
      year         = {2021},
      note         = {#EA:C020#LA:C020#},
      abstract     = {Current guidelines for lung cancer screening via low-dose
                      computed tomography recommend annual screening for all
                      candidates meeting basic eligibility criteria. However, lung
                      cancer risk of eligible screening participants can vary
                      widely, and further risk stratification could be used to
                      individually optimize screening intervals in view of
                      expected benefits, possible harms and financial costs. To
                      this effect, models have been developed in the US National
                      Lung Screening Trial based on self-reported lung cancer risk
                      factors and imaging data. We evaluated these models using
                      data from an independent screening trial in Germany.We
                      examined the Polynomial model by Schreuder et al., the Lung
                      Cancer Risk Assessment Tool extended by CT characteristics
                      (LCRAT + CT) by Robbins et al., and a criterion of presence
                      vs. absence of pulmonary nodules ≥4 mm (Patz et al.),
                      applied to sub-sets of screening participants according to
                      eligibility criteria. Discrimination was evaluated via the
                      receiver operating characteristic curve. Delayed diagnoses
                      and false positive results were calculated at various
                      thresholds of predicted risk. Model calibration was assessed
                      by comparing mean predicted risk versus observed
                      incidence.One thousand five hundred and six participants
                      were eligible for the validation of the LCRAT + CT model,
                      and 1,889 for the validation of the Polynomial model and
                      Patz criterion, yielding areas under the receiver operating
                      characteristic curve of 0.73 $(95\%$ CI: 0.63, 0.82), 0.75
                      (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping
                      $50\%$ annual screenings (participants within the 5 lowest
                      risk deciles by LCRAT + CT in any round or by the Polynomial
                      model; baseline screening round), would have avoided $75\%$
                      $(21.9\%,$ $98.7\%)$ and $40\%$ $(21.8\%,$ $61.1\%)$ false
                      positive screen tests and delayed $10\%$ $(1.8\%,$ $33.1\%)$
                      or no $(0\%,$ $32.1\%)$ diagnoses, respectively. Using the
                      Patz criterion, referring $63.2\%$ $(61.0\%$ to $65.4\%)$ of
                      participants to biennial screening would have avoided $4\%$
                      $(0.2\%$ to $22.3\%)$ of false positive screen tests but
                      delayed $55\%$ $(24.6\%$ to $81.9\%)$ diagnoses.In this
                      German trial, the LCRAT + CT and Polynomial models showed
                      useful discrimination of screening participants for one-year
                      lung cancer risk following CT examination. Our results
                      illustrate the remaining heterogeneity in risk within
                      screening-eligible subjects and the trade-off between a
                      low-frequency screening approach and delayed detection.},
      keywords     = {Lung cancer screening (Other) / risk prediction (Other) /
                      screening intervals (Other) / validation (Other)},
      cin          = {C020 / E010},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331 / I:(DE-He78)E010-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33889511},
      pmc          = {pmc:PMC8044498},
      doi          = {10.21037/tlcr-20-1173},
      url          = {https://inrepo02.dkfz.de/record/168497},
}