Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening

SIMPLE SUMMARY: Lung cancer screening with low-dose CT (LDCT) has been shown to significantly reduce cancer-related mortality and is recommended by the United States Preventive Services Task Force (USPSTF). With pending recommendation in Europe and millions of patients enrolling in the program, deep learning algorithms could reduce the number of false positive and negative findings. Therefore, we evaluated the cost-effectiveness of using an AI algorithm for the initial screening scan using a Markov simulation. We found that AI support at initial screening is a cost-effective strategy up to a cost of USD 1240 per patient screening, given a willingness-to-pay of USD 100,000 per quality-adjusted life years (QALYs). ABSTRACT: Background: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist’s performance in lung nodule detection and classification. Therefore, the aim of this study was to evaluate the cost-effectiveness of an AI-based system in the context of baseline lung cancer screening. Methods: In this retrospective study, a decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Literature research was performed to determine model input parameters. Model uncertainty and possible costs of the AI-system were assessed using deterministic and probabilistic sensitivity analysis. Results: In the base case scenario CT + AI resulted in a negative incremental cost-effectiveness ratio (ICER) as compared to CT only, showing lower costs and higher effectiveness. Threshold analysis showed that the ICER remained negative up to a threshold of USD 68 for the AI support. The willingness-to-pay of USD 100,000 was crossed at a value of USD 1240. Deterministic and probabilistic sensitivity analysis showed model robustness for varying input parameters. Conclusion: Based on our results, the use of an AI-based system in the initial low-dose CT scan of lung cancer screening is a feasible diagnostic strategy from a cost-effectiveness perspective.