RT Journal Article SR Electronic T1 Analysis of exhaled breath with electronic nose and diagnosis of lung cancer by multifactorial logistic regression analysis JF European Respiratory Journal JO Eur Respir J FD European Respiratory Society SP P2889 VO 42 IS Suppl 57 A1 Maris Bukovskis A1 Gunta Strazda A1 Normunds Jurka A1 Uldis Kopeika A1 Ainis Pirtnieks A1 Liga Balode A1 Jevgenija Aprinceva A1 Inara Kantane A1 Immanuels Taivans YR 2013 UL http://erj.ersjournals.com/content/42/Suppl_57/P2889.abstract AB BackgroundExhaled breath of lung cancer patients contains specific pattern of volatile organic compounds (VOCs).ObjectiveThe aim of our study was to test the potential of multifactorial logistic regression (MLRA) analysis in diagnosis of lung cancer.MethodsExhaled breath of morphologically verified lung cancer patients (cancer group) and mixed group of patients with COPD, asthma, pneumonia, bronchiectasis and healthy volunteers (no cancer group) was examined. Exhaled air was collected using standardized method and sampled by electronic nose (Cyranose 320). Optimal detector parameter combination and methematical model for discrimination of lung cancer was calculated by MLRA backward stepwise method. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV) of the method in the training group of smokers and nonsmokers was calculated.ResultsTotal 475 patients, out of them 252 lung cancer patients and 223 patients with different lung diseases and healthy volunteers, and 265 current nonsmokers and 210 smokers, were recruited in the study.View this table:Classification of cases in nonsmokersView this table:Classification of cases in smokersConclusionsFinding of optimal detector parameter combination and division of patients in smokers and nonsmokers give very high lung cancer prediction accuracy with MLRA.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/