Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study
File(s)
Published version
Author(s)
Muller, DC
Johansson, M
Brennan, P
Type
Journal Article
Abstract
Purpose:
Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study.
Methods:
This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]).
Results:
During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10−13). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]).
Conclusion:
A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.
Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study.
Methods:
This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]).
Results:
During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10−13). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]).
Conclusion:
A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.
Date Issued
2017-01-17
Date Acceptance
2017-01-01
Citation
JOURNAL OF CLINICAL ONCOLOGY, 2017, 35 (8), pp.861-869
ISSN
0732-183X
Publisher
American Society of Clinical Oncology
Start Page
861
End Page
869
Journal / Book Title
JOURNAL OF CLINICAL ONCOLOGY
Volume
35
Issue
8
Copyright Statement
© 2017 American Society of Clinical Oncology.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000396367800009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Oncology
SURFACTANT PROTEIN-B
FAMILY-HISTORY
RESPIRATORY-DISEASE
SUSCEPTIBILITY LOCUS
SMOKING-CESSATION
CUMULATIVE RISK
SCREENING TRIAL
SMOKERS
WOMEN
ASSOCIATION
Oncology & Carcinogenesis
1112 Oncology And Carcinogenesis
Publication Status
Published