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A prospective study on artificial intelligence guided lung cancer screening for high-risk never smokers in Hong Kong

Description: Objective: Emerging evidence suggests that lung cancer screening with low dose computed tomography (LDCT) of thorax may improve survival among high-risk never smokers. However, high cost and shortage of radiologists remain major barriers to implementing LDCT screening. This study aims to prospectively evaluate the clinical utility of artificial intelligence (AI)-guided lung cancer screening and to establish a screening model for high-risk never smokers in Hong Kong Hypothesis: The integration of AI into LDCT analysis is both efficacious and cost-effective for lung cancer screening. Design and Subjects: This is a single arm, prospective lung cancer screening study targeting never smokers aged 50-75 years with at least one first-degree relative diagnosed with lung cancer. Study Instruments and Interventions: Participants will undergo LDCT of the thorax, with images analyzed by an AI-guided lung nodule detection programme. Subsequent management will be guided by AI-generated risk assessments. Cost-effectiveness analysis and study on acceptability to AI-guided lung cancer screening among service providers and screening participants will be conducted in parallel. Main Outcome Measures: The primary outcome is the prevalence of early stage lung cancer detected through screening. Secondary outcomes include cost-effectiveness and acceptability among healthcare providers and participants. Expected Results: The findings are expected to generate local evidence to support the implementation of an AI-guided lung cancer screening model for high-risk never smokers in Hong Kong.

Funder: Health and Medical Research Fund

Amount: HKD 11,940,000

Funding type: grant

Funder scheme: Commissioned Study on the Use of AI in Lung Cancer Screening for High-Risk Non-smokers

Start date: 2025-01-01

Investigator: Siu Ching LI

Institution: The Chinese University of Hong Kong

Funded outputs count: 0

Funder award ID: AI-LCS-001

OpenAlex ID: G7657890592