By: Jade Parker, Future Science Group
In a study published recently in Scientific Reports, researchers from The University of Texas MD Anderson Cancer Center (TX, USA) have developed an integrative risk prediction model that could be utilized to better predict lung cancer risk in never, light and heavy smokers.
Light and never smokers are not generally candidates for low-dose CT screening and while the majority of lung cancer cases are diagnosed in heavy smokers, in the USA up to 20% of lung cancer cases occur in those who do not smoke, suggesting that better risk stratification is required across all smoking types.
Lead author Xifeng Wu of MD Anderson Cancer Center commented: “Currently, there are no criteria to select high-risk individuals for lung cancer in never smokers. Lung cancer screening criteria is based only on age and smoking information, but from this study we can differentiate risk in those who have never smoked in addition to light and heavy smokers.”
In this Taiwanese prospective cohort study, the researchers went beyond analyzing just the participant’s age, gender and smoking habits. They also studied the levels of four serum biomarkers, analyzed personal and family cancer history, body mass index and performed a lung function test.
Utilizing this data, the team were able to obtain 5- and 10-year lung cancer risk probabilities. They then stratified heavy smokers into high-risk subgroups and identified light and never smokers who were at risk of developing lung cancer.
Wu concluded: “Our model was able to stratify light and never smokers into groups with dramatically different probabilities of developing lung cancer over time. According to our results, a small number of never smokers have lung cancer risks as high as some heavy smokers.”
The research group hope that their study will influence the selection criteria and process utilized when choosing individuals for targeted lung cancer screening strategies. However, they also highlighted that the work is limited by being carried out in a distinct Taiwanese population. Additional validations in independent cohorts will be required to assess the true predictive ability of this model across non-Asian populations.