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

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.

A diagnostic and economic evaluation of the complex artificial intelligence algorithm aimed to detect 10 pathologies on the chest CT images

“…Threshold analysis showed that ICER remained negative until the $68 threshold to support AI applications. Thus, using a mono system for analyzing LDCT data using AI for SRL is a reasonable diagnostic strategy for cost-effectiveness [54].…”

Detection of Lung Cancer Cases in Chest CT Scans Utilizing Artificial Intelligence: A Retrospective Analysis of Data During the COVID-19 Pandemic

“…A recent study conducted by Ziegelmayer S and colleagues assessed the cost-effectiveness of implementing an AI algorithm in the initial round of LC screening through the use of Markov simulation. The findings of the study indicate that incorporating AI assistance during the initial screening phase proves to be a cost-effective approach, with a maximum cost of 1,240 USD per patient, assuming a willingness to pay 100,000 USD for quality-adjusted life years (QALYs) [15]. QALYs are a measure of disease burden that combines both the quality and quantity of life lived, and are commonly used in economic evaluations of medical interventions.…”

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

“…The diagnostic accuracy in guiding genotype directed therapy was 97%, and the average time to start treatment was less than one day ( 68 ). CT combined with AI resulted in a negative incremental cost-effectiveness ratio (ICER) compared to CT only, showing lower costs and higher effectiveness ( 69 ). The lowest net cost savings using AI-informed management strategies are estimated at $72 per screened patient ( 70 ).…”