Abstract

Background: The pattern of volatile organic compounds in the exhaled breath of lung cancer patients may be unique. Novel sensor systems that detect patterns of volatiles have been developed. One of these sensor systems, a colorimetric sensor array, has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge. The colors of these spots change based on the chemicals they come in contact with. In this proof of principle study, we assess the ability of this sensor system to detect a pattern of volatiles unique to lung cancer.

Methods: Individuals with lung cancer, other lung diseases, and healthy controls performed tidal breathing of room air for 12 minutes while exhaling into a device designed to draw their breath across a colorimetric sensor array. The color changes that occurred for each individual were converted into a numerical vector. The vectors were analyzed statistically, using a random forests technique, to determine if lung cancer could be predicted from the sensor responses.

Results: 143 individuals participated in the study. 49 with non-small cell lung cancer, 18 COPD, 15 IPF, 20 PAH, 20 sarcoid, and 21 controls. A prediction model was developed using observations from 70% of the subjects. This model was able to predict the presence of lung cancer in the remaining 30% of the subjects with a sensitivity of 73.3% and specificity of 72.4% (p=0.01).

Conclusions: The unique chemical signature of the breath of lung cancer patients is able to be detected with moderate accuracy by a colorimetric sensor array.