MA02.12 UC Screen California: A Statewide Participatory Informatics Approach to Lung Cancer Screening

Canadian Early Detection of Lung Cancer, Pittsburgh Lung Screening Study, and Toronto Screening Program, we used Sybil to estimate lung cancer risk for year 1 to 6 and estimated the area under the receiving operating characteristics curve (AUC).We also evaluated the AUC by nodule diameter, solidity, and nodule presence.Results: Based on the 3 studies, the AUC for Sybil was 0.93 for lung cancer diagnosed within 1 year, down to 0.79 within 6 years (see Table 1).When restricting the analysis to those with pulmonary nodules, the AUCs of Sybil remained comparable.However, when focusing on individuals with no baseline nodules, the AUC from Sybil reduced to 0.65.When stratified by nodule diameter, Sybil performs better among larger nodules (maximum diameter > 10 mm), with AUCs of 0.91 for year 1 vs 0.86 in those with small nodules.When stratified by solidity, Sybil performs better in ground glass opacity nodules, with AUC above 0.93 in years 1 and 2, but it performs better in partially solid nodules in years 3 to 6, with AUC above 0.81.To assess if Sybil can predict lung cancer risk in the absence of nodules, we used LDCT scans for patients with a known lung cancer diagnosis but no apparent nodules at baseline.91 patients did not have nodules at baseline but were diagnosed with lung cancer between 1 to 6 years.83 of 91 had low Sybil scores at baseline, which suggests that Sybil's predictive performance may be suboptimal in the absence of nodules.Conclusions: We evaluated the predictive performance of Sybil in three independent lung cancer screening studies.The performance was best within 1 year.A decline in accuracy was observed for predictions after 3 years.We also found that Sybil may not be able to predict lung cancer risk without the presence of nodules and may have limited clinical use for fast-growing nodules, not present at baseline.