TY - JOUR T1 - Detection of early stage lung cancer by electronic nose JF - European Respiratory Journal JO - Eur Respir J VL - 42 IS - Suppl 57 SP - P2888 AU - Maris Bukovskis AU - Gunta Strazda AU - Normunds Jurka AU - Uldis Kopeika AU - Ainis Pirtnieks AU - Liga Balode AU - Jevgenija Aprinceva AU - Inara Kantane AU - Immanuels Taivans Y1 - 2013/09/01 UR - http://erj.ersjournals.com/content/42/Suppl_57/P2888.abstract N2 - BackgroundThe diagnosis of early stage lung cancer is very essential and substantially determins the five year life expectancy.ObjectiveThe aim of our study was to prove the potential of exhaled breath analysis to discriminate early stage lung cancer.MethodsExhaled 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.Results40 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.View this table:Confusion matrix (Support Vector Machine)ConclusionsExhaled 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.AcknowledgementsStudy was sponsored by ERAF activity 2.1.1.1.0 Project Nb. 2010/0303/2DP/2.1.1.1.0/10/APIA/VIAA/043/ ER -