Investigating AI's potential in risk-stratifying lung cancer screenings for extending screening intervals and reducing workload: A retrospective analysis of a tertiary hospital cohort

Objective: We aimed to 1) evaluate whether AI algorithms could identify a very low-risk sub-population Lung-RADS 1/2 cases suitable for a safe switch to biennial screening, and 2) externally validate Sybil, an imaging-based deep learning lung cancer risk prediction tool. Materials and methods: We gathered all lung cancer screenings (LCSs) from a tertiary safety-net hospital (n=3120, up to September 2024), manually extracted relevant clinical data, and analyzed Sybil's performance across various subgroups, with a specific focus on Lung-RADS 1 and 2 cases. We simulated LCS interval extension using Sybil and the Brock model risk scores calibrated on a subset of the National Lung Screening Trial (NLST). Confidence intervals were obtained through bootstrapping. Results: The median follow-up was 2.3 years (interquartile range 1.5-3.1), with a total 6-year cancer incidence rate of 2.3% (72/3120). Lung-RADS 1 and 2 cases comprised 90.5% of our scans. Sybil's overall area under the curve (AUC) ranged from 0.819 to 0.912; while its 2-year AUC for Lung-RADS 1 and 2 cases was 0.746. In the screening interval extension simulation, calibrating for a 30% shift to biennial screening translated into 33.8% of negative screens in our data being safely extended using Sybil, with no delayed cancers. The Brock model demonstrated an AUC of 0.513 on our data, with a sensitivity of 18.2% using the 1.5% threshold. Conclusion: Sybil has the potential to safely extend LCS intervals for low-risk individuals, reducing radiology workload and opening spots for life-saving new screenings.