Improving lung cancer screening selection: the HUNT Lung Cancer Risk Model for ever-smokers versus the NELSON and 2021 USPSTF criteria in the Cohort of Norway (CONOR), a Population-based Prospective Study

Background Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here we compare the performance of the HUNT Lung Cancer Model (HUNT LCM) versus the NELSON and 2021 USPSTF criteria regarding LC risk prediction and efficiency Methods We used linked data from ten Norwegian prospective population-based cohorts, Cohort of Norway (CONOR). The study included 44,831 ever-smokers where 686 (1.5%) subjects developed LC, median follow-up time was 11.6 years (0.01-20.8 years). Results Within six years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased LC prediction rate with 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity (P<0.001 and P=0.028), and reduced the number needed to predict one LC case (29 vs. 40, P<0.001 and 36 vs. 40, P=0.02), respectively. Applying the HUNT LCM six-year 0.98% risk score as a cutoff (14.0% of ever-smokers) predicted 70.7% of all LC, increasing LC prediction rate with 89.2% and 18.9% versus the NELSON and 2021 USPSTF, respectively (both P<0.001). Conclusions The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving prediction of LC diagnosis and may be used as a validated clinical tool for screening selection.