Download PDF
Semin Respir Crit Care Med 2011; 32(1): 003-009
DOI: 10.1055/s-0031-1272864
© Thieme Medical Publishers

Estimating Individual Risk for Lung Cancer

Carol J. Etzel1 , Peter B. Bach2
  • 1Department of Epidemiology, UT MD Anderson Cancer Center, Houston, Texas
  • 2Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
Further Information

Publication History

Publication Date:
15 April 2011 (online)

    Permissions and Reprints

ABSTRACT

Lung cancer risk prediction models hold the promise of improving patient care and streamlining research. The ultimate goal of these models is to inform clinicians as to which interventions their individual patients should receive to reduce lung cancer–associated morbidity and mortality. In this paper, we discuss the history and current state of lung cancer prediction models, focusing on three models: the Bach model, the Spitz model, and the Liverpool Lung Project (LLP) model. We also discuss the prospects for further development of improved prediction models for lung cancer risk. Although current models can identify those smokers at highest risk for lung cancer, these models are presently of limited use in the clinical setting. Nevertheless, lung cancer risk prediction models can be used during study enrollment to select more appropriate study subjects, and may eventually be useful in identifying patients for lung cancer screening or to receive chemoprevention.

KEYWORDS

Risk models - discriminatory power - accuracy - clinical utility - screening - chemoprevention

REFERENCES

  • 1 Risch H A, Howe G R, Jain M, Burch J D, Holowaty E J, Miller A B. Are female smokers at higher risk for lung cancer than male smokers? A case-control analysis by histologic type.  Am J Epidemiol. 1993;  138 (5) 281-293
    CrossrefPubMedGoogle Scholar
  • 2 American Cancer Society .Cancer Facts and Figures 2010. Atlanta, GA: American Cancer Society; 2010
    Google Scholar
  • 3 Amos C I, Xu W, Spitz M R. Is there a genetic basis for lung cancer susceptibility?.  Recent Results Cancer Res. 1999;  151 3-12
    PubMedGoogle Scholar
  • 4 Peto R, Darby S, Deo H, Silcocks P, Whitley E, Doll R. Smoking, smoking cessation, and lung cancer in the UK since 1950: combination of national statistics with two case-control studies.  BMJ. 2000;  321 (7257) 323-329
    CrossrefPubMedGoogle Scholar
  • 5 Gail M H, Brinton L A, Byar D P et al.. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.  J Natl Cancer Inst. 1989;  81 (24) 1879-1886
    CrossrefPubMedGoogle Scholar
  • 6 Tyrer J, Duffy S W, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors.  Stat Med. 2004;  23 (7) 1111-1130
    CrossrefPubMedGoogle Scholar
  • 7 Tice J A, Cummings S R, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population.  Breast Cancer Res Treat. 2005;  94 (2) 115-122
    CrossrefPubMedGoogle Scholar
  • 8 Imperiale T F, Wagner D R, Lin C Y, Larkin G N, Rogge J D, Ransohoff D F. Risk of advanced proximal neoplasms in asymptomatic adults according to the distal colorectal findings.  N Engl J Med. 2000;  343 (3) 169-174
    CrossrefPubMedGoogle Scholar
  • 9 Selvachandran S N, Hodder R J, Ballal M S, Jones P, Cade D. Prediction of colorectal cancer by a patient consultation questionnaire and scoring system: a prospective study.  Lancet. 2002;  360 (9329) 278-283
    CrossrefPubMedGoogle Scholar
  • 10 Fears T R, Guerry IV D, Pfeiffer R M et al.. Identifying individuals at high risk of melanoma: a practical predictor of absolute risk.  J Clin Oncol. 2006;  24 (22) 3590-3596
    CrossrefPubMedGoogle Scholar
  • 11 Cho E, Rosner B A, Feskanich D, Colditz G A. Risk factors and individual probabilities of melanoma for whites.  J Clin Oncol. 2005;  23 (12) 2669-2675
    PubMedGoogle Scholar
  • 12 Hartge P, Whittemore A S, Itnyre J, McGowan L, Cramer D. The Collaborative Ovarian Cancer Group . Rates and risks of ovarian cancer in subgroups of white women in the United States.  Obstet Gynecol. 1994;  84 (5) 760-764
    PubMedGoogle Scholar
  • 13 Eastham J A, May R, Robertson J L, Sartor O, Kattan M W. Development of a nomogram that predicts the probability of a positive prostate biopsy in men with an abnormal digital rectal examination and a prostate-specific antigen between 0 and 4 ng/mL.  Urology. 1999;  54 (4) 709-713
    CrossrefPubMedGoogle Scholar
  • 14 Wu X, Lin J, Grossman H B et al.. Projecting individualized probabilities of developing bladder cancer in white individuals.  J Clin Oncol. 2007;  25 (31) 4974-4981
    CrossrefPubMedGoogle Scholar
  • 15 Colditz G A, Atwood K A, Emmons K et al.. Harvard report on cancer prevention volume 4: Harvard Cancer Risk Index. Risk Index Working Group, Harvard Center for Cancer Prevention.  Cancer Causes Control. 2000;  11 (6) 477-488
    CrossrefPubMedGoogle Scholar
  • 16 Bach P B, Kattan M W, Thornquist M D et al.. Variations in lung cancer risk among smokers.  J Natl Cancer Inst. 2003;  95 (6) 470-478
    CrossrefPubMedGoogle Scholar
  • 17 Spitz M R, Hong W K, Amos C I et al.. A risk model for prediction of lung cancer.  J Natl Cancer Inst. 2007;  99 (9) 715-726
    CrossrefPubMedGoogle Scholar
  • 18 Cassidy A, Duffy S W, Myles J P, Liloglou T, Field J K. Lung cancer risk prediction: a tool for early detection.  Int J Cancer. 2007;  120 (1) 1-6
    PubMedGoogle Scholar
  • 19 Woloshin S, Schwartz L M, Welch H G. The risk of death by age, sex, and smoking status in the United States: putting health risks in context.  J Natl Cancer Inst. 2008;  100 (12) 845-853
    CrossrefPubMedGoogle Scholar
  • 20 Chen J, Pee D, Ayyagari R et al.. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density.  J Natl Cancer Inst. 2006;  98 (17) 1215-1226
    CrossrefPubMedGoogle Scholar
  • 21 Cook N R. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve.  Clin Chem. 2008;  54 (1) 17-23
    PubMedGoogle Scholar
  • 22 Harrell Jr F E, Lee K L, Mark D B. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med. 1996;  15 (4) 361-387
    CrossrefPubMedGoogle Scholar
  • 23 Cronin K A, Gail M H, Zou Z, Bach P B, Virtamo J, Albanes D. Validation of a model of lung cancer risk prediction among smokers.  J Natl Cancer Inst. 2006;  98 (9) 637-640
    CrossrefPubMedGoogle Scholar
  • 24 D'Amelio Jr A M, Cassidy A, Asomaning K et al.. Comparison of discriminatory power and accuracy of three lung cancer risk models.  Br J Cancer. 2010;  103 (3) 423-429
    CrossrefPubMedGoogle Scholar
  • 25 Marshall R J. Displaying clinical data relationships using scaled rectangle diagrams.  Stat Med. 2001;  20 (7) 1077-1088
    CrossrefPubMedGoogle Scholar
  • 26 Marshall R J. Scaled rectangle diagrams can be used to visualize clinical and epidemiological data.  J Clin Epidemiol. 2005;  58 (10) 974-981
    CrossrefPubMedGoogle Scholar
  • 27 Marshall R J. Cardiovascular risk can be represented by scaled rectangle diagrams.  J Clin Epidemiol. 2009;  62 (9) 998-1000
    CrossrefPubMedGoogle Scholar
  • 28 Abidoye O, Ferguson M K, Salgia R. Lung carcinoma in African Americans.  Nat Clin Pract Oncol. 2007;  4 (2) 118-129
    PubMedGoogle Scholar
  • 29 Etzel C J, Kachroo S, Liu M et al.. Development and validation of a lung cancer risk prediction model for African-Americans.  Cancer Prev Res (Phila). 2008;  1 (4) 255-265
    CrossrefPubMedGoogle Scholar
  • 30 Bain C, Feskanich D, Speizer F E et al.. Lung cancer rates in men and women with comparable histories of smoking.  J Natl Cancer Inst. 2004;  96 (11) 826-834
    CrossrefPubMedGoogle Scholar
  • 31 Bach P B. Lung cancer screening.  J Natl Compr Canc Netw. 2008;  6 (3) 271-275
    PubMedGoogle Scholar
  • 32 Bach P B, Jett J R, Pastorino U, Tockman M S, Swensen S J, Begg C B. Computed tomography screening and lung cancer outcomes.  JAMA. 2007;  297 (9) 953-961
    CrossrefPubMedGoogle Scholar
  • 33 Infante M, Cavuto S, Lutman F R DANTE Study Group et al. A randomized study of lung cancer screening with spiral computed tomography: three-year results from the DANTE trial.  Am J Respir Crit Care Med. 2009;  180 (5) 445-453
    CrossrefPubMedGoogle Scholar
  • 34 van Iersel C A, de Koning H J, Draisma G et al.. Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON).  Int J Cancer. 2007;  120 (4) 868-874
    CrossrefPubMedGoogle Scholar
  • 35 Janssens ACJW, Moonesinghe R, Yang Q, Steyerberg E W, van Duijn C M, Khoury M J. The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases.  Genet Med. 2007;  9 (8) 528-535
    CrossrefPubMedGoogle Scholar
  • 36 Alberg A J, Samet J M. Epidemiology of lung cancer. In: Sadler M J, Callaero B, Strain J J, eds. Encyclopedia of Human Nutrition. London, England: Academic; 1998: 235-248
    Google Scholar
  • 37 Männistö S, Smith-Warner S A, Spiegelman D et al.. Dietary carotenoids and risk of lung cancer in a pooled analysis of seven cohort studies.  Cancer Epidemiol Biomarkers Prev. 2004;  13 (1) 40-48
    CrossrefPubMedGoogle Scholar
  • 38 Gail M H. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.  J Natl Cancer Inst. 2008;  100 (14) 1037-1041
    CrossrefPubMedGoogle Scholar
  • 39 Gail M H. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model.  J Natl Cancer Inst. 2009;  101 (13) 959-963
    CrossrefPubMedGoogle Scholar
  • 40 Spitz M R, Amos C I, D'Amelio Jr A, Dong Q, Etzel C. Re: discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.  J Natl Cancer Inst. 2009;  101 (24) 1731-1732, author reply 1732
    CrossrefPubMedGoogle Scholar
  • 41 Jakobsdottir J, Gorin M B, Conley Y P, Ferrell R E, Weeks D E. Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers.  PLoS Genet. 2009;  5 (2) e1000337
    CrossrefPubMedGoogle Scholar
  • 42 Janssens ACJW, Aulchenko Y S, Elefante S, Borsboom GJJM, Steyerberg E W, van Duijn C M. Predictive testing for complex diseases using multiple genes: fact or fiction?.  Genet Med. 2006;  8 (7) 395-400
    CrossrefPubMedGoogle Scholar
  • 43 Spitz M R, Etzel C J, Dong Q et al.. An expanded risk prediction model of lung cancer.  Can Prev Res. 2008;  1 (4) 250-254
    PubMedGoogle Scholar

Peter B BachM.D. 

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center

1275 York Ave., New York, NY 10065

Email: bachp@mskcc.org

      >