Detection of early stage lung cancer by electronic nose

Abstract

Background

The diagnosis of early stage lung cancer is very essential and substantially determins the five year life expectancy.

Objective

The aim of our study was to prove the potential of exhaled breath analysis to discriminate early stage lung cancer.

Methods

Exhaled breath of morphologically verified stage 1-4 lung cancer patients, mixed group of patients with COPD, asthma, bronchiectasis and healthy volunteers (no cancer group) was examined. Subjects inspired filtered air by tidal breathing for 5 minutes, and a single expiratory vital capacity was collected and sampled by electronic nose (Cyranose 320). Maximum (Rmax), AUC (∑0-60”) and tgα0-60” of the curves were analyzed by support vector machine. Age, smoking history (pack years) and ambient temperature °C were included as continuous predictors of the diagnosis.

Results

40 patients with stage 1 or 2, 49 with stage 3, 46 with stage 4 lung cancer and 109 patient with other lung diseases and healthy volunteers were recruited. 72.5% (29 out of 40) patients with stage 1-2 lung cancer and 82.3% (106 out of 135) patients with any stage of lung cancer were predicted correctly.

Conclusions

Exhaled breath analysis by electronic nose using support vector pattern recognition method is able to discriminate early stage lung cancer from healthy subjects and patients with different lung diseases.

Acknowledgements

Study was sponsored by ERAF activity 2.1.1.1.0 Project Nb. 2010/0303/2DP/2.1.1.1.0/10/APIA/VIAA/043/