Lung cancer screening programs may be improved with the use of risk prediction models that incorporate lung function, according to the results of a study published in the Journal of Clinical Oncology.1

Previous models of lung cancer risk prediction used factors such as sex, variables related to smoking history and nicotine addiction, medical history, and family history of lung cancer. For this study, researchers attempted to develop, internally validate, and evaluate the incorporation of lung function (forced expiratory volume in 1 second [FEV1]) into the prediction models.

Using data from the UK Biobank prospective cohort study, the researchers analyzed 502,321 participants with no previous diagnosis of lung cancer. Most patients were aged 40 to 70. At baseline, participants completed a questionnaire of general health and medical history, lifestyle and diet, and family history of disease. They then underwent a physical assessment that included FEV1 assessment using spirometry. The researchers estimated the 2-year probability of lung cancer, accounting for the competing risk for death.

Overall, the researchers collected data for 1,469,518 person-years, during which there were 738 lung cancer diagnoses. They found that maximum FEV1 was strongly inversely associated with the risk for lung cancer regardless of the patient’s smoking status.

The new model had excellent discrimination with a concordance (c)-statistic of 0.85. Evaluation of internal validation showed that the model would discriminate well when applied to new data outside of the study.

Overall, the full model that included FEV1 had modestly superior discriminatory power to one designed solely on the basis of the questionnaire variables. The full model that included lung function had better discrimination than the standard lung cancer screening eligibility criteria.

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According to the researchers, “using this model to establish eligibility for screening could lead to substantially greater sensitivity than the National Lung Screening Trial screening criteria in the UK population.”

Reference

  1. Muller DC, Johansson M, Brennan P. Lung cancer risk prediction model incorporating lung function: development and validation in the UK Biobank Prospective Cohort Study. J Clin Oncol. 2017 Jan 16. doi: 10.1200/JCO.2016.69.2467 [Epub ahead of print]