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Research Article

Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort

Kuanrong Li, Anika Hüsing, Disorn Sookthai, Manuela Bergmann, Heiner Boeing, Nikolaus Becker and Rudolf Kaaks
Kuanrong Li
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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  • For correspondence: k.li@dkfz.der.kaaks@dkfz.de
Anika Hüsing
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Disorn Sookthai
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Manuela Bergmann
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
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Heiner Boeing
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
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Nikolaus Becker
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Rudolf Kaaks
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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  • For correspondence: k.li@dkfz.der.kaaks@dkfz.de
DOI: 10.1158/1940-6207.CAPR-14-0424 Published September 2015
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    Figure 1.

    Net benefits obtained from different selection approaches (A) and the cumulative distribution of 5-year absolute risk estimates (B).

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  • Table 1.

    Eligibility criteria used in lung cancer screening trials

    Eligibility criteria
    TrialAge, ySmoking history
    NELSON50–74≥15 cigarettes/d for 25 y; or ≥10 cigarettes/d for at least 30 y; if former smokers, quitting time ≤10 y.
    LUSI50–69≥15 cigarettes/d for at least 25 y; or ≥10 cigarettes/d for at least 30 y; if former smokers, quitting time ≤10 y.
    DLCST50–69Pack-years ≥20; if former smokers, age at quitting >50 y and quitting time <10 y
    ITALUNG55–69Pack-years ≥20 since the last 10 years; if former smokers, quitting time <10 y.
    DANTE60–74Pack-years ≥20.
    NLST55–74Pack-years ≥30; if former smokers, quitting time ≤15 y.
  • Table 2.

    Predictors included in the Bach, Spitz, LLP, and PLCOM2012 model and the availability in the EPIC-Germany cohort

    PredictorsBachSpitzLLPPLCOM2012Availability in EPIC-Germany
    AgeXXaXaXX
    SexXXaXaX
    Smoking statusXX
    Cigarettes smoked/dXXX
    Smoking durationXXXX
    Duration of cessationXXX
    Age at cessationXX
    Pack-yearsXX
    Asbestos exposureXXXX
    Dust exposureX
    PneumoniaX
    COPDX
    EmphysemaX
    Hay feverXX
    Prior diagnosis of malignant tumorXXXb
    Family history of cancerXX
    Family history of lung cancerXX
    BMIXX
    EducationXX
    • ↵aAge and sex were not included the model as predictors. However, the effects of age and sex were incorporated into the risk prediction by using the age- and sex-specific lung cancer incidence rates to estimate the baseline hazards.

    • ↵bThis information was available but subjects with prior diagnosis of malignant tumor were excluded from the analysis.

  • Table 3.

    Baseline characteristics of the study population by sex among the ever smokers in the EPIC-Germany cohort (N = 20,700)

    Men (n = 12,565)Women (n = 8,135)
    Age (range)53.2 (40.0–69.4)49.6 (40.0–69.0)
    LC in the first 5-years of follow-up7616
    Age at diagnosis of lung cancer63.5 (42.8–76.6)62.0 (44.7–75.6)
    Current smokers (%)4,648 (37.0)3,649 (44.8)
    Duration of smoking (y)24.0 (0.5–53.5)23.0 (0.5–52.0)
    No. of cigarettes/d15.0 (0.04–85.0)8.8 (0.02–72.6)
    Pack-years12.0 (0.05–127.5)5.6 (0.05–66.0)
    Quitting time (former smokers)16.0 (0.1–43.8)14.1 (0.1–43.2)
    Asbestos exposure151 (1.2)8 (0.1)
    Hay fever1,253 (10.0)1,253 (15.4)
    BMI (kg/m2)26.9 (14.5–55.2)25.0 (15.1–58.7)
    Family history of cancer3,882 (30.9)3,129 (38.5)
    College education4,609 (36.7)2,051 (25.2)

    Abbreviation: LC, lung cancer.

    • Table 4.

      Validation performance of the lung cancer risk models applied to the ever smokers in the EPIC-Germany cohort (N = 20,700)

      Discrimination (C-index, 95% CI)Calibrationa(E/O, 95% CI)
      Bach model0.81 (0.76–0.86)0.88 (0.72–1.08)
      Spitz model0.78 (0.73–0.83)3.75 (3.06–4.60)
      LLP model0.79 (0.73–0.83)1.12 (0.92–1.37)
      PLCOM20120.81 (0.76–0.86)1.03 (0.87–1.23)
      • ↵aThe expected and observed numbers of lung cancer are 81/92 (Bach), 345/92 (Spitz), 103/92 (LLP), and 130/126 (6-year risk, PLCOM2012).

    • Table 5.

      Comparison of the lung cancer risk prediction models and the eligibility criteria among ever smokers in the EPIC-Germany cohort (N = 20,700)

      Criteria/modelThreshold (%)Cases includedaSensitivity (%)Specificity (%)PPV (%)cNPV (%)
      npositive = 3,409
       NELSON/LUSI—5863.0483.741.700.20
       Bach0.7649325761.9683.731.670.20
       Spitz3.5118134953.2683.691.440.25
       LLP0.8526615458.7083.721.580.20
       PLCOM20101.0069916166.3083.751.790.18
      npositive = 2,931
       DLCST—5964.1386.062.010.18
       Bach0.8988355761.9686.051.940.20
       Spitz3.7420994548.9186.01.540.26
       LLP0.9400015155.4386.021.740.23
       PLCOM20101.1277465964.1386.062.010.18
      npositive = 2,170
       ITALUNG—5155.4389.722.350.22
       Bach1.1782585155.4389.722.350.22
       Spitz4.1809234144.5689.671.890.28
       LLP1.1799834650.089.692.120.25
       PLCOM20101.7265095559.8089.742.530.20
      npositive = 1,500
       DANTE—3639.1392.902.400.29
      npositive = 1,458
       NLST—4245.6593.132.880.26
       Bach1.5531464447.8393.143.020.25
       Spitz4.7239093436.9693.092.330.30
       LLP1.5286214043.4893.122.740.27
       PLCOM20102.1103654650.093.153.160.24
      • ↵aCases that were diagnosed the first 5 years of follow-up.

    Additional Files

    • Figures
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    • Supplementary Data

      • Supplementary Table 1, Figures 1-4 - Supplementary Table 1, Figures 1-4. Supplementary Table 1. Pairwise kappa before and after overlapping age range was applied (the lower triangle) and the net reclassification index (NRI, %, the upper triangle). Supplementary Figure 1. Participants selected for LDCT screening using different eligibility criteria among ever smokers in the EPIC-Germany cohort (N = 20,700). Supplementary Figure 2. ROC curves of the lung cancer risk prediction models applied to ever smokers in the EPIC-Germany cohort (N = 20,700). Supplementary Figure 3. The cumulative distribution of 5-year absolute risk estimates in the EPIC-Germany cohort (N = 20,700) and the LUSI study (N = 33,621). Supplementary Figure 4. The cumulative distribution of 5-year absolute risk estimates before and after missing risk factors were simulated (ever smokers in EPIC-Germany, N = 20,700).
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    Cancer Prevention Research: 8 (9)
    September 2015
    Volume 8, Issue 9
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    Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort
    Kuanrong Li, Anika Hüsing, Disorn Sookthai, Manuela Bergmann, Heiner Boeing, Nikolaus Becker and Rudolf Kaaks
    Cancer Prev Res September 1 2015 (8) (9) 777-785; DOI: 10.1158/1940-6207.CAPR-14-0424

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    Selecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort
    Kuanrong Li, Anika Hüsing, Disorn Sookthai, Manuela Bergmann, Heiner Boeing, Nikolaus Becker and Rudolf Kaaks
    Cancer Prev Res September 1 2015 (8) (9) 777-785; DOI: 10.1158/1940-6207.CAPR-14-0424
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