Background: Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that may be used as non-invasive markers of lung disease. The electronic nose analyzes VOCs by composite nano-sensor arrays with learning algorithms. It has been shown that an electronic nose can distinguish the VOCs pattern in exhaled breath of lung cancer patients from healthy controls. We hypothesized that an electronic nose can discriminate patients with lung cancer from COPD patients and healthy controls by analyzing the VOC-profile in exhaled breath.
Methods: 30 subjects participated in a cross-sectional study: 10 patients with non-small cell lung cancer (NSCLC, [age 66.4+/-9.0, FEV(1) 86.3+/-20.7]), 10 patients with COPD (age 61.4+/-5.5, FEV(1) 70.0+/-14.8) and 10 healthy controls (age 58.3+/-8.1, FEV(1) 108.9+/-14.6). After 5 min tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, subjects performed a single vital capacity maneuver to collect dried exhaled air into a Tedlar bag. The bag was connected to the electronic nose (Cyranose 320) within 10 min, with VOC-filtered room air as baseline. The smellprints were analyzed by onboard statistical software.
Results: Smellprints from NSCLC patients clustered distinctly from those of COPD subjects (cross validation value [CVV]: 85%; M-distance: 3.73). NSCLC patients could also be discriminated from healthy controls in duplicate measurements (CVV: 90% and 80%, respectively; M-distance: 2.96 and 2.26).
Conclusion: VOC-patterns of exhaled breath discriminates patients with lung cancer from COPD patients as well as healthy controls. The electronic nose may qualify as a non-invasive diagnostic tool for lung cancer in the future.