Korean J Fam Pract.  2024 Jun;14(2):90-97. 10.21215/kjfp.2024.14.2.90.

A Lung Cancer Risk Prediction Model from Healthy Korean Adults: A Single Center Cohort Study

Affiliations
  • 1Department of Family Medicine, Wonkwang University Sanbon Hospital, Gunpo, Korea
  • 2Department of Economics, Texas A&M University, Texas, USA
  • 3Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
  • 4Workplace Health Institute, Total Health Care Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
  • 5Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Abstract

Background
Lung cancer has a high incidence and mortality worldwide, and smoking, age, sex, and body mass index are known risk factors. Using a health examination cohort, we constructed a comprehensive lung cancer risk-prediction model.
Methods
This study comprised 308,804 adults aged 20 years and older who underwent health examinations at one general hospital in Korea, from 2011 to 2018. We developed a lung cancer risk prediction model using a multivariate Cox proportional hazards regression analysis for lung cancer risk factors and estimated the hazard ratios and coefficients. The model evaluation included discrimination and calibration assessments.
Results
Among the 308,804 adults in the study cohort, there were 338 (0.11%) patients lung cancer, with 215 males (0.07% of 169,420 males) and 123 females (0.04% of 139,384 females). The prevalence of lung cancer was higher in males and females aged over 60 years. Age, sex, body mass index, and smoking behavior were identified as risk factors for lung cancer prevalence in this model through multivariate Cox proportional hazards analysis. The C-statistic of the development cohort was 0.785 (0.749, 0.821) and that of the validation cohort was 0.823 (0.769, 0.878).
Conclusion
Our lung cancer risk prediction model showed statistical significance, similar to previous prediction models, among variables that included young age, female sex, and body mass index. Future improvements should focus on population-wide applicability and associated health examination policies.

Keyword

Lung Cancer; Risk Factor; Prediction Model; Age; Body Mass Index; Smoking
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