Kan AI identificere tidligstadie lungekræft ud fra åndedrætsbiomarkører ved hjælp af bærbare elektroniske næser ?

Følgende flygtige organiske forbindelser i udåndingsluften ændrer sig i tilfælde af tidligt lungekræft, selv før billeddiagnostik opdager tumorer. AI-drevne elektroniske næser kunne analysere åndedrætsprøver på klinikker eller apoteker. Dette kunne reducere behovet for invasive biopsi og CT-scanninger. Miljømæssige faktorer som rygning eller luftforurening kan dog forvirre resultaterne.

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.