Design and rationale of the ZORALCS study: An implementation study of lung cancer screening by low-dose computed tomography coupled to a smoking cessation randomized controlled trial in the Flemish region

This paper outlines the design of the ZORALCS study, which evaluates the feasibility of implementing Lung cancer screening (LCS) using Low-dose computed tomography (LDCT) in the Flemish region of Belgium. Despite high lung cancer mortality rates in Belgium, a large-scale screening program is lacking; thus, this study analyzes participation rates and effectiveness among 25,885 high-risk individuals aged 55–74. Participants are screened using the PLCOm2012 and HUNT models and undergo annual LDCT scans for two years. During scan interpretation, Artificial Intelligence (AI) software serves as a second reader to assist with nodule detection, automated volume analysis, and volume doubling time calculations to enhance accuracy. Additionally, the TAMIRO-STOP randomized controlled trial is integrated to provide smoking cessation support for current smokers. The findings are expected to provide foundational data for future national lung cancer screening policies in Belgium. This study highlights how AI can be integrated into real-world screening workflows, supporting detection, quantification, and consistency in high-volume LDCT programs. In this context, Coreline Soft’s AI software is applied as a second reader to assist with nodule detection and volumetric analysis.