Respiratory Function as a Prognostic Factor for Lung Cancer in Screening and General Populations.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Rationale: Despite advancements in screening, lung cancer remains the leading cause of cancer-related mortality globally. Objectives: To investigate respiratory function as a prognostic factor for survival in the UK Biobank, a population-based cohort of more than 500,000 participants, and the NLST (National Lung Screening Trial), a high-risk screening population of more than 50,000 screenees. Methods: Participants with an incident lung cancer diagnosis and spirometry-assessed lung function were included. Lung cancer was measured as the ratio of forced expiratory volume in 1 second (FEV1) and forced vital capacity and percentage of predicted FEV1. Multivariable Cox proportional hazards models were fitted to estimate the impact of lung function on 5-year overall survival in populations with different baseline lung cancer risks. Results: A total of 2,690 and 609 patients were included in the analysis from the UK Biobank and the NLST, respectively. In the UK Biobank, a higher percentage of predicted FEV1 and ratio were associated with better survival after lung cancer diagnosis, with hazard ratios of 0.97 (95% confidence interval [CI], 0.95-1.00 per 10% increase) and 0.95 (95% CI, 0.90-1.00 per 10% increase), respectively. No statistically significant results were found when assessing the data from the NLST study. Conclusions: Impaired lung function was associated with poorer survival for patients with lung cancer in the general population, although this was less clear in a high-risk, screening-eligible population. This highlights the potential clinical importance of respiratory function as a prognostic factor in lung cancer in the general population and presents a possibility for personalized cancer management.
- NLST-349: LDCT Pulmonary Nodule Assessment Model Based on Multi-Omics Approach (Rayjean Hung - 2017)