Lung cancer risk prediction to select smokers for screening CT : a model based on the Italian COSMOS trial

Screening with low-dose helical computed tomography (CT) has been shown to significantly reduce lung cancer mortality but the optimal target population and time interval to subsequent screening are yet to be defined. We developed two models to stratify individual smokers according to risk of developing lung cancer. We first used the number of lung cancers detected at baseline screening CT in the 5203 asymptomatic participants of the COSMOS trial to recalibrate the Bach model, which we propose using to select smokers for screening. Next we incorporated lung nodule characteristics and presence of emphysema identified at baseline CT into the Bach model and propose the resulting multivariable model to predict lung cancer risk in screened smokers after baseline CT. Age and smoking exposure were the main determinants of lung cancer risk. The recalibrated Bach model accurately predicted lung cancers detected during the first year of screening. Presence of non-solid nodules (RR=10.1, 95% CI=5.57-18.5), nodule size >8 mm (RR=9.89, 95% CI=5.84-16.8) and emphysema (RR=2.36, 95% CI=1.59-3.49) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the multivariable model (c-index=0.759, internal validation). The recalibrated Bach model appears suitable for selecting the higher risk population for recruitment for large-scale CT screening. The Bach model incorporating CT findings at baseline screening could help defining the time interval to subsequent screening in individual participants. Further studies are necessary to validate these models.