Training and Validating a Portable Electronic Nose for Lung Cancer Screening

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Abstract

Introduction:

Profiling volatile organic compounds in exhaled breath enables the diagnosis of several types of cancer. In this study we investigated whether a portable point-of-care version of an electronic nose (e-nose) (Aeonose, [eNose Company, Zutphen, the Netherlands]) is able to discriminate between patients with lung cancer and healthy controls on the basis of their volatile organic compound pattern.

Methods:

In this study, we used five e-nose devices to collect breath samples from patients with lung cancer and healthy controls. A total of 60 patients with lung cancer and 107 controls exhaled through an e-nose for 5 minutes. Patients were assigned either to a training group for building an artificial neural network model or to a blinded control group for validating this model.

Results:

For differentiating patients with lung cancer from healthy controls, the results showed a diagnostic accuracy of 83% with a sensitivity of 83%, specificity of 84%, and area under the curve of 0.84. Results for the blinded group showed comparable results, with a sensitivity of 88%, specificity of 86%, and diagnostic accuracy of 86%.

Conclusion:

This feasibility study showed that this portable e-nose can properly differentiate between patients with lung cancer and healthy controls. This result could have important implications for future lung cancer screening. Further studies with larger cohorts, including also more participants with early-stage tumors, should be performed to increase the robustness of this noninvasive diagnostic tool and to determine its added value in the diagnostic chain for lung cancer.

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