Electronic-Nose Technology for Lung Cancer Detection: A Non-Invasive Diagnostic Revolution

Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, primarily due to late-stage diagnosis and the absence of effective early detection methods.

This review aims to explore the principles, technological advancements, current limitations, and future prospects of electronic nose (E-nose) systems in the early detection of lung cancer.

The review analyzes recent literature on E-nose devices that detect volatile organic compounds (VOCs) in exhaled breath, focusing on their integration with artificial intelligence (AI) and machine learning for pattern recognition and diagnostic classification.

E-noses have demonstrated high sensitivity and specificity in differentiating cancerous from non-cancerous breath samples. However, challenges such as sensor stability, lack of standardization in breath collection, demographic variability, and the need for large training datasets for AI models limit their clinical adoption.

Despite current limitations, E-nose technology shows strong potential as a rapid, non-invasive, and cost-effective tool for early LC screening. Enhancing sensor durability, improving data processing, and conducting large-scale validation studies are critical next steps. Integration with imaging and molecular biomarkers may further improve diagnostic accuracy and clinical utility.