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

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Abstract

ObjectiveWe 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 methodsWe 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 Sybils 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. ResultsThe 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. Sybils 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. ConclusionSybil has the potential to safely extend LCS intervals for low-risk individuals, reducing radiology workload and opening spots for life-saving new screenings. Key pointsO_LIWe investigated whether AI risk stratification tools could identify the lowest-risk individuals undergoing lung cancer screening suitable for switching to a biennial screening schedule. C_LIO_LIIn our simulations, Sybil reduced follow-up screening load by at least 30% through switching the lowest-risk Lung-RADS 1 and 2 cases to biennial screening without delayed cancer diagnoses. C_LIO_LIVery low-risk individuals, as predicted by AI algorithms, could potentially be placed on biennial screening schedules, reducing radiology department workload and freeing time for new patients. C_LI

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