Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • "Best of" Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Cancer Prevention Research
Cancer Prevention Research
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • "Best of" Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Research Article

A Risk Model for Lung Cancer Incidence

Clive Hoggart, Paul Brennan, Anne Tjonneland, Ulla Vogel, Kim Overvad, Jane Nautrup Østergaard, Rudolf Kaaks, Federico Canzian, Heiner Boeing, Annika Steffen, Antonia Trichopoulou, Christina Bamia, Dimitrios Trichopoulos, Mattias Johansson, Domenico Palli, Vittorio Krogh, Rosario Tumino, Carlotta Sacerdote, Salvatore Panico, Hendriek Boshuizen, H. Bas Bueno-de-Mesquita, Petra H.M. Peeters, Eiliv Lund, Inger Torhild Gram, Tonje Braaten, Laudina Rodríguez, Antonio Agudo, Emilio Sánchez-Cantalejo, Larraitz Arriola, Maria-Dolores Chirlaque, Aurelio Barricarte, Torgny Rasmuson, Kay-Tee Khaw, Nicholas Wareham, Naomi E. Allen, Elio Riboli and Paolo Vineis
Clive Hoggart
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paul Brennan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anne Tjonneland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ulla Vogel
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kim Overvad
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jane Nautrup Østergaard
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rudolf Kaaks
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Federico Canzian
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Heiner Boeing
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Annika Steffen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonia Trichopoulou
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christina Bamia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dimitrios Trichopoulos
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mattias Johansson
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Domenico Palli
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vittorio Krogh
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rosario Tumino
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlotta Sacerdote
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Salvatore Panico
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hendriek Boshuizen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
H. Bas Bueno-de-Mesquita
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Petra H.M. Peeters
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eiliv Lund
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Inger Torhild Gram
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tonje Braaten
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Laudina Rodríguez
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonio Agudo
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Emilio Sánchez-Cantalejo
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Larraitz Arriola
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maria-Dolores Chirlaque
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aurelio Barricarte
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Torgny Rasmuson
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kay-Tee Khaw
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicholas Wareham
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Naomi E. Allen
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elio Riboli
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paolo Vineis
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1158/1940-6207.CAPR-11-0237 Published June 2012
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    Predicted probabilities of 1 year lung cancer incidence, for a variety of smoking profiles, assuming an average smoking intensity of 20 cpd. A, probability of lung cancer in current smokers by age, for different ages of commencement of smoking. B, probability of lung cancer in current smokers by smoking duration, for different ages of commencement of smoking. C, probability of lung cancer in former smokers by age, for different ages of commencement of smoking assuming a smoking duration of 20 to 30 years. D, probability of lung cancer in former smokers by age, for different durations of smoking assuming commencement of smoking between 22 and 26 years old.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    Calibration of risk models for (A) current smokers, (B) former smokers, and (C) the combined group of ever smokers. Plots show predicted and observed estimates of 1-year lung cancer incidence in the test set by deciles of predicted risk. Probabilities were calculated for each year each individual was followed. Bars at each decile show the 95% confidence interval for the predicted probability. The observed number of cases and person-years of exposure in the top 3 deciles are shown at the bottom of the figure.

Tables

  • Figures
  • Table 1.

    Distribution of population variables in the training and test sets by smoking and disease status

    Training setTest set
    Current smokersFormer smokersCurrent smokersFormer smokers
    No lung cancerLung cancerNo lung cancerLung cancerNo lung cancerLung cancerNo lung cancerLung cancerMissing (%)
    Total73,677 (98.9)820 (1.1)77,328 (99.61)304 (0.39)8,187 (98.89)92 (1.11)8,593 (99.61)34 (0.39)
    Number of genotyped individuals, n (%)508 (44.8)626 (55.2)678 (74.34)234 (25.66)57 (47.9)62 (52.1)72 (72.73)27 (27.27)98.83
    Sociodemographic
    Sex, n (%)
     Male45,647 (99.23)354 (0.77)43,178 (99.76)104 (0.24)5,073 (99.24)39 (0.76)4,870 (99.75)12 (0.25)0
     Female70,169 (98.84)820 (1.16)74,009 (99.59)304 (0.41)7,774 (98.83)92 (1.17)8,225 (99.59)34 (0.41)
    Dead at censoring, n (%)a3,508 (100)0 (0)3,319 (100)0 (0)413 (100)0 (0)368 (100)0 (0)0
    Age, mean (SD), y57.5 (8.8)62 (7.3)60.5 (9.5)65.6 (8.8)57.4 (8.8)60.6 (8.2)60.5 (9.6)65.8 (8.5)0
    Age at recruitment, mean (SD), y49.5 (8.7)57.2 (7.2)52.5 (9.4)60.8 (8.4)49.5 (8.8)56.1 (8.1)52.5 (9.5)60.8 (8.3)0
    Follow-up, mean (SD), y7.9 (2)4.8 (2.6)8 (2)4.8 (2.6)7.9 (2)4.5 (2.9)8 (2)4.9 (2.3)0
    BMI, mean (SD), kg/m225.7 (4.1)25.7 (4.5)26.4 (4.1)26.7 (3.6)25.7 (4.2)26 (4.3)26.3 (4.1)27 (2.5)12.56
    Education, n (%)
     High school and below60,415 (98.84)710 (1.16)56,190 (99.54)259 (0.46)6,642 (98.74)85 (1.26)6,201 (99.57)27 (0.43)4.68
     Greater than high school11,817 (99.32)81 (0.68)17,532 (99.89)20 (0.11)1,390 (99.57)6 (0.43)1,984 (99.7)6 (0.3)
    Medical history
    Hay fever, n (%)
     No15,925 (99.1)145 (0.9)16,526 (99.73)45 (0.27)1,784 (98.84)21 (1.16)1,815 (99.78)4 (0.22)73.53
     Yes2,664 (99.48)14 (0.52)3,963 (99.67)13 (0.33)301 (99.34)2 (0.66)467 (99.57)2 (0.43)
    Asthma, n (%)
     No30,788 (99.16)260 (0.84)27,690 (99.73)76 (0.27)3,393 (99.09)31 (0.91)3,071 (99.71)9 (0.29)61.46
     Yes1,521 (98.51)23 (1.49)2,103 (98.92)23 (1.08)161 (98.17)3 (1.83)269 (99.26)2 (0.74)
    Family history of cancer, n (%)
     No9,730 (98.81)117 (1.19)10,880 (99.32)74 (0.68)1,098 (99.01)11 (0.99)1,235 (99.2)10 (0.8)84.02
     Yes1,051 (98.32)18 (1.68)1,409 (99.09)13 (0.91)121 (99.18)1 (0.82)157 (98.74)2 (1.26)
    Smoking exposures
    Smoking intensity, mean (SD), cpd13.5 (7.5)17.6 (7.4)13.1 (9.2)17.8 (10.9)13.5 (7.4)17.2 (7.9)13 (9.2)17.1 (11.6)0
    Smoking duration, mean (SD), y30.3 (9.7)39.5 (8.2)19 (10.7)31.4 (12.1)30.2 (9.8)38.8 (8.4)18.9 (10.7)31.7 (11)0
    Quit time, mean (SD), y0 (0.1)0 (0.1)15 (9.9)11.9 (9.3)0 (0.1)0 (0.1)15 (9.9)10.8 (7.4)0
    Age start smoking, mean (SD), y27.2 (6.1)22.5 (5.1)26.5 (4.8)22.3 (5.2)27.2 (6.2)21.8 (4.2)26.5 (4.9)23.2 (5.9)0
    Cigarettes per day
     ≤1528,222 (98.21)515 (1.79)29,174 (99.37)186 (0.63)3,203 (98.31)55 (1.69)3,242 (99.42)19 (0.58)0
     >1545,455 (99.33)305 (0.67)48,154 (99.76)118 (0.24)4,984 (99.26)37 (0.74)5,351 (99.72)15 (0.28)
    Occupational exposures
    Silica, n (%)
     Not exposed44,057 (98.64)609 (1.36)43,604 (99.51)216 (0.49)4,944 (98.58)71 (1.42)4,870 (99.47)26 (0.53)42.06
     Exposed1,125 (97.74)26 (2.26)1,153 (99.31)8 (0.69)118 (98.33)2 (1.67)104 (99.05)1 (0.95)
    PAH, n (%)
     Not exposed39,750 (98.72)514 (1.28)39,620 (99.51)194 (0.49)4,419 (98.55)65 (1.45)4,412 (99.53)21 (0.47)42.06
     Exposed5,432 (97.82)121 (2.18)5,137 (99.42)30 (0.58)643 (98.77)8 (1.23)562 (98.94)6 (1.06)
    Metal, n (%)
     Not exposed32,596 (98.65)447 (1.35)34,035 (99.56)150 (0.44)3,661 (98.79)45 (1.21)3,831 (99.56)17 (0.44)46.19
     Exposed6,827 (98)139 (2)7,521 (99.31)52 (0.69)760 (97.31)21 (2.69)806 (99.02)8 (0.98)
    Asbestos, n (%)
     Not exposed39,734 (98.73)513 (1.27)38,974 (99.54)182 (0.46)4,425 (98.75)56 (1.25)4,334 (99.54)20 (0.46)42.06
     Exposed5,448 (97.81)122 (2.19)5,783 (99.28)42 (0.72)637 (97.4)17 (2.6)640 (98.92)7 (1.08)
    • ↵aNumbers are taken at time of censoring, cases were censored at diagnosis, before death.

  • Table 2.

    Weibull parameters and HRs of smoking intensity used in Equation (A) for the lung cancer incidence and death models for current and former smokers

    Current smokers
    Lung cancer incidenceDeath
    HR (95% CI) β1PHR (95% CI) β2P
    Smoking intensity ≤ 151.111 (1.084–1.139)<10−161.051 (1.041–1.062)<10−16
    Weibull hazard parametersWeibull hazard parameters
    StrataShape (95% CI) λ1Scale (95% CI) γ1Shape (95% CI) λ2Scale (95% CI) γ2
     t ≤ 183.819 (3.750–3.869)0.999 (0.772–1.162)3.690 (3.659–3.713)1.220 (1.105–1.302)
     18 < t ≤ 204.056 (3.966–4.121)1.071 (0.870–1.215)3.774 (3.748–3.793)1.312 (1.210–1.384)
     20 < t ≤ 224.230 (4.134–4.299)1.298 (1.144–1.408)3.859 (3.839–3.873)1.560 (1.489–1.611)
     22 < t ≤ 244.339 (4.234–4.414)1.518 (1.374–1.621)3.944 (3.924–3.958)1.775 (1.715–1.818)
     24 < t ≤ 264.380 (4.258–4.468)1.679 (1.519–1.794)3.979 (3.958–3.995)1.925 (1.865–1.969)
     26 < t ≤ 284.567 (4.361–4.714)1.517 (1.294–1.677)4.049 (4.017–4.071)1.854 (1.773–1.911)
     28 < t ≤ 304.506 (4.278–4.670)1.615 (1.344–1.809)4.096 (4.049–4.130)1.838 (1.727–1.917)
     30 < t4.504 (4.317–4.637)1.684 (1.454–1.848)4.069 (4.040–4.090)1.912 (1.835–1.966)
    Former smokers
    Lung cancer incidenceDeath
    HR (95% CI) β1PHR (95% CI) β2P
    Smoking intensity ≤ 151.043 (1.012–1.076)0.0071.015 (1.006–1.023)<10−16
    Weibull hazard parametersWeibull hazard parameters
    StrataShape (95% CI) λ1Scale (95% CI) γ1Shape (95% CI) λ2Scale (95% CI) γ2
     t ≤ 22; s ≤ 204.987 (4.372–5.427)0.750 (0.359–1.029)3.754 (3.722–3.776)1.210 (1.128–1.269)
     20 < s ≤ 304.723 (4.242–5.066)0.819 (0.409–1.112)3.750 (3.726–3.767)1.511 (1.428–1.570)
     30 < s4.321 (4.140–4.450)1.032 (0.733–1.246)3.800 (3.785–3.810)1.669 (1.603–1.716)
     22 < t ≤ 26; s ≤ 205.179 (4.537–5.638)1.165 (0.795–1.430)4.008 (3.975–4.032)1.621 (1.555–1.668)
     20 < s ≤ 304.786 (4.335–5.108)1.353 (0.989–1.612)3.975 (3.942–3.998)1.742 (1.663–1.799)
     30 < s ≤ 364.651 (4.132–5.022)1.460 (0.945–1.829)3.954 (3.921–3.977)1.835 (1.734–1.907)
     36 < s ≤ 424.654 (4.034–5.097)1.318 (0.639–1.804)3.928 (3.903–3.945)2.129 (2.018–2.208)
     42 < s4.366 (4.090–4.563)1.662 (1.136–2.038)3.982 (3.961–3.998)2.150 (2.040–2.229)
     t > 26; s ≤ 205.563 (4.668–6.202)1.088 (0.640–1.409)4.123 (4.087–4.148)1.769 (1.698–1.820)
     20 < s ≤ 304.680 (4.223–5.007)1.705 (1.249–2.032)4.058 (4.026–4.082)1.865 (1.781–1.926)
     30 < s ≤ 364.491 (4.062–4.797)1.879 (1.312–2.283)4.020 (3.986–4.044)2.148 (2.027–2.234)
     36 < s ≤ 424.241 (3.990–4.420)2.336 (1.754–2.751)3.999 (3.968–4.021)2.360 (2.213–2.465)
     42 < s4.177 (4.052–4.266)2.574 (2.123–2.896)4.041 (4.007–4.066)2.490 (2.295–2.629)

    NOTE: Parameter estimates are based on maximum likelihood estimates, 95% CIs are in parentheses. Time zero set to 35 years old.

    Abbreviations: s, smoking duration; t, age start smoke.

    • Table 3.

      HRs for effect of exposures in current and former smokers estimated in the training set and their predictive value in the test set

      HR (95% CI)PP for improvement in AUCtdNRI
      ChangeP
      CovariateCurrentFormerCurrentFormerCurrentFormerCurrentFormerCurrentFormer
      Sex: female 1, male 0.1.35 (1.16–1.57)1.2 (0.91–1.59)0.0001260.194−0.885−0.346−0.3210.990.937
      BMI0.963 (0.946–0.98)0.96 (0.929–0.992)3.7 × 10−50.0148−−−0.0709−0.2060.6870.82
      Education level (greater than high school)0.944 (0.751–1.19)0.436 (0.275–0.691)0.620.0004180.243−0.235−0.07630.010.637
      Hay fever0.593 (0.335–1.05)0.901 (0.494–1.64)0.07280.734++0.2330.120.1390.366
      Asthma0.85 (0.546–1.32)1.58 (0.961–2.6)0.4740.071++0.0437−0.01270.3460.504
      Family history of cancer1.27 (0.758–2.11)1.23 (0.694–2.16)0.3680.484++−0.217−0.2350.90.845
      Chr15q251.13 (1.01–1.27)1.14 (0.933–1.38)0.03360.202−0.7260.3090.4060.3810.23
      Chr5p150.954 (0.845–1.08)1.06 (0.865–1.3)0.4420.5720.7650.8851.3−0.4950.0670.808
      Occupational exposures
       Silica0.893 (0.602–1.33)0.851 (0.349–2.07)0.5740.722++−0.0252−0.09070.6840.945
       PAH0.988 (0.808–1.21)0.869 (0.586–1.29)0.9060.4850.671−0.0177−0.1940.4280.871
       Metal0.961 (0.794–1.16)1.23 (0.866–1.74)0.680.249−0.308−0.3850.2810.9960.06
       Asbestos0.943 (0.775–1.15)1.05 (0.738–1.49)0.5580.784−0.603−0.240.2960.9860.029

      NOTE: Exposure effects are conditional on lifetime exposure to cigarette smoke. P value for improvement in AUC and tdNRI compares predictive performance of model with and without covariate (tdNRI P values were calculated by permutation). AUC P values are not shown for covariates whose inclusion decreased the AUC (denoted by −) and there were fewer than 5 exposed cases (denoted by +).

      • Table 4.

        Predictive performance, measured by the AUC, of our model and the Bach model for predicting 1- and 5-year cancer risk in the EPIC test set

        Current smokersFormer smokersEver smokersBach high-risk group
        One year
         Controls8,1878,59316,7804,934
         Cases923412682
         New0.824 (0.783–0.865)0.830 (0.762–0.899)0.843 (0.810–0.875)0.753 (0.700–0.806)
         Bach0.732 (0.683–0.780)0.787 (0.710–0.864)0.775 (0.737–0.813)0.656 (0.595–0.717)
         P1.25 × 10−50.09573.91 × 10−61.61 × 10−4
        Five years
         Controls7,4446,53513,9794,049
         Cases43105337
         New0.767 (0.701–0.832)0.715 (0.532–0.898)0.787 (0.728–0.847)0.681 (0.597–0.765)
         Bach0.749 (0.686–0.813)0.753 (0.583–0.922)0.743 (0.685–0.802)0.589 (0.510–0.669)
         P0.3620.118a0.0240.0035

        NOTE: Bach high-risk group are individuals between 50 and 75 years old who have smoked 10 to 60 cpd for 25 to 55 years. Also shown is the P value for improvement in AUC of our model compared with the Bach model.

        aAUC for 5-year prediction of former smokers is higher using Bach model, therefore P value refers to superiority of Bach model relative to the new model.

        PreviousNext
        Back to top
        Cancer Prevention Research: 5 (6)
        June 2012
        Volume 5, Issue 6
        • Table of Contents
        • Table of Contents (PDF)
        • About the Cover

        Sign up for alerts

        View this article with LENS

        Open full page PDF
        Article Alerts
        Sign In to Email Alerts with your Email Address
        Email Article

        Thank you for sharing this Cancer Prevention Research article.

        NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

        Enter multiple addresses on separate lines or separate them with commas.
        A Risk Model for Lung Cancer Incidence
        (Your Name) has forwarded a page to you from Cancer Prevention Research
        (Your Name) thought you would be interested in this article in Cancer Prevention Research.
        CAPTCHA
        This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
        Citation Tools
        A Risk Model for Lung Cancer Incidence
        Clive Hoggart, Paul Brennan, Anne Tjonneland, Ulla Vogel, Kim Overvad, Jane Nautrup Østergaard, Rudolf Kaaks, Federico Canzian, Heiner Boeing, Annika Steffen, Antonia Trichopoulou, Christina Bamia, Dimitrios Trichopoulos, Mattias Johansson, Domenico Palli, Vittorio Krogh, Rosario Tumino, Carlotta Sacerdote, Salvatore Panico, Hendriek Boshuizen, H. Bas Bueno-de-Mesquita, Petra H.M. Peeters, Eiliv Lund, Inger Torhild Gram, Tonje Braaten, Laudina Rodríguez, Antonio Agudo, Emilio Sánchez-Cantalejo, Larraitz Arriola, Maria-Dolores Chirlaque, Aurelio Barricarte, Torgny Rasmuson, Kay-Tee Khaw, Nicholas Wareham, Naomi E. Allen, Elio Riboli and Paolo Vineis
        Cancer Prev Res June 1 2012 (5) (6) 834-846; DOI: 10.1158/1940-6207.CAPR-11-0237

        Citation Manager Formats

        • BibTeX
        • Bookends
        • EasyBib
        • EndNote (tagged)
        • EndNote 8 (xml)
        • Medlars
        • Mendeley
        • Papers
        • RefWorks Tagged
        • Ref Manager
        • RIS
        • Zotero
        Share
        A Risk Model for Lung Cancer Incidence
        Clive Hoggart, Paul Brennan, Anne Tjonneland, Ulla Vogel, Kim Overvad, Jane Nautrup Østergaard, Rudolf Kaaks, Federico Canzian, Heiner Boeing, Annika Steffen, Antonia Trichopoulou, Christina Bamia, Dimitrios Trichopoulos, Mattias Johansson, Domenico Palli, Vittorio Krogh, Rosario Tumino, Carlotta Sacerdote, Salvatore Panico, Hendriek Boshuizen, H. Bas Bueno-de-Mesquita, Petra H.M. Peeters, Eiliv Lund, Inger Torhild Gram, Tonje Braaten, Laudina Rodríguez, Antonio Agudo, Emilio Sánchez-Cantalejo, Larraitz Arriola, Maria-Dolores Chirlaque, Aurelio Barricarte, Torgny Rasmuson, Kay-Tee Khaw, Nicholas Wareham, Naomi E. Allen, Elio Riboli and Paolo Vineis
        Cancer Prev Res June 1 2012 (5) (6) 834-846; DOI: 10.1158/1940-6207.CAPR-11-0237
        del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
        • Tweet Widget
        • Facebook Like
        • Google Plus One

        Jump to section

        • Article
          • Abstract
          • Introduction
          • Methods
          • Results
          • Discussion
          • Disclosure of Potential Conflicts of Interest
          • Grant Support
          • Acknowledgments
          • References
        • Figures & Data
        • Info & Metrics
        • PDF
        Advertisement

        Related Articles

        Cited By...

        More in this TOC Section

        • mt-sDNA Specificity in 45–49 Year-Olds
        • Targeting CD40 and PD-1/PD-L1 Inhibits OPL Progression to OSCC
        • Revisiting a clinical diagnosis of NF1 for other syndromes
        Show more Research Articles
        • Home
        • Alerts
        • Feedback
        • Privacy Policy
        Facebook   Twitter   LinkedIn   YouTube   RSS

        Articles

        • Online First
        • Current Issue
        • Past Issues

        Info for

        • Authors
        • Subscribers
        • Advertisers
        • Librarians

        About Cancer Prevention Research

        • About the Journal
        • Editorial Board
        • Permissions
        • Submit a Manuscript
        AACR logo

        Copyright © 2021 by the American Association for Cancer Research.

        Cancer Prevention Research
        eISSN: 1940-6215
        ISSN: 1940-6207

        Advertisement