Kan AI vroegstadium-longkanker identificeren aan de hand van adem biomarkers met draagbare elektronische neuzen ?

Bepaalde vluchtige organische verbindingen in uitgeademde adem veranderen bij aanwezigheid van vroeg stadium longkanker, zelfs voordat beeldvorming tumoren detecteert. AI-gestuurde elektronische neuzen zouden ademmonsters kunnen analyseren in klinieken of apotheken. Dit zou de afhankelijkheid van invasieve biopsieën en CT-scans kunnen verminderen. Omgevingsfactoren zoals roken of luchtvervuiling kunnen echter de resultaten verstoren.

Background

Researchers have demonstrated that portable electronic noses (e-noses) can detect volatile organic compounds (VOCs) in exhaled breath with promising sensitivity and specificity for early-stage lung cancer screening. A 2022 meta-analysis reported pooled sensitivity of about 85% and specificity of 87% across multiple studies using machine-learning models trained on breath-chemistry data. Certain volatile organic compounds in exhaled breath change in presence of early lung cancer, even before imaging detects tumors, and AI-powered e-noses could analyze breath samples in clinics or pharmacies, reducing reliance on invasive diagnostics. However, environmental factors like smoking or air pollution may confound results. Furthermore, real-world deployment faces challenges such as sensor drift, environmental confounders like smoking or diet, and the need for larger, multi-center validation cohorts. Regulatory approval remains limited to a few devices with narrow indications, underscoring the gap between promising research and routine clinical use.