Literature DB >> 16670389

Validation of a model of lung cancer risk prediction among smokers.

Kathleen A Cronin1, Mitchell H Gail, Zhaohui Zou, Peter B Bach, Jarmo Virtamo, Demetrius Albanes.   

Abstract

The Bach model was developed to predict the absolute 10-year risk of developing lung cancer among smokers by use of participants in the Carotene and Retinol Efficacy Trial of lung cancer prevention. We assessed the validity of the Bach model among 6239 smokers from the placebo arm of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The expected numbers of lung cancer cases and deaths without lung cancer were calculated from the Bach model and compared with the observed numbers of corresponding events over 10 years. We found that the risk model slightly underestimated the observed lung cancer risk (number of lung cancers expected/number observed = 0.89, 95% confidence interval [CI] = 0.80 to 0.99) over 10 years. The competing risk portion of the model substantially underestimated risk of non-lung cancer mortality (number of non-lung cancer deaths expected/number observed = 0.61, 95% CI = 0.57 to 0.64) over 10 years. The age-specific concordance indices for 10-year predictions were 0.77 (95% CI = 0.70 to 0.84), 0.59 (95% CI = 0.53 to 0.65), 0.62 (95% CI = 0.57 to 0.67), and 0.57 (95% CI = 0.49 to 0.67) for the age groups 50-54, 55-59, 60-64, and 65-69 years, respectively. Periodic radiographic screening in the ATBC Study may explain why slightly more cancers were observed than expected from the Bach model.

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Year:  2006        PMID: 16670389     DOI: 10.1093/jnci/djj163

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  31 in total

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Review 4.  Biomarkers of risk to develop lung cancer in the new screening era.

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Review 5.  Oral Cell DNA Adducts as Potential Biomarkers for Lung Cancer Susceptibility in Cigarette Smokers.

Authors:  Stephen S Hecht
Journal:  Chem Res Toxicol       Date:  2016-12-01       Impact factor: 3.739

6.  Lung cancer screening: how do we make it better?

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7.  Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network.

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8.  Selection criteria for lung-cancer screening.

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9.  Pattern of antioxidant and DNA repair gene expression in normal airway epithelium associated with lung cancer diagnosis.

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Review 10.  [Lung cancer screening - risk stratification : Who should undergo screening?].

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