Lung cancer is a very common malignancy with a low five-year survival rate. Artificial olfactory sensor (electronic nose) is a tool that recently has been studied as a probable optimal screening tool for early detection of lung cancer, but still no statistical method has been put forward as the preferable one. The aim of the study was to explore the use of logistic regression analysis (LRA) to analyse patients' exhaled breath samples with electronic nose in order to differentiate lung cancer patients (regardless of the stage of the cancer) from patients with other lung diseases and healthy individuals. Patients with histologically or cytologically verified, untreated lung cancer, patients with other lung diseases such as benign lung tumors, chronic obstructive pulmonary disease, asthma, pneumonia, etc, and healthy volunteers were enrolled in the study, in total 252 cancer patients and 223 patients without cancer. Breath sample collection and analysis were performed with Cyranose 320 sensor device and data further analysed using LRA. The LRA correctly differentiated lung cancer patients from no-cancer patients. The overall sensitivity in detecting patients having cancer was 95.8% for smokers and 96.2% for non-smokers and the overall specificity was 90.6% for non-smokers and 92.3% for smokers. Exhaled breath analysis by electronic nose using LRA is able to discriminate lung cancer patients from patients with other lung diseases and from healthy individuals.

Lung cancer is one of the most common malignancies and has a low 5-year survival rate. There are no cheap, simple and widely available screening methods for the early diagnostics of lung cancer. The aim of this study was to determine whether analysis of exhaled breath with an artificial olfactory sensor using support vector analysis can differentiate patients with lung cancer from healthy individuals and patients with other lung diseases, regardless of the stage of lung cancer and the most common comorbidities. Patients with histologically or cytologically verified lung cancer, healthy volunteers and patients with other lung diseases (e.g. chronic obstructive pulmonary disease (COPD), asthma, pneumonia, pulmonary embolism, benign lung tumors) were enrolled in the study. Breath sample collection and analysis with a Cyranose 320 sensor device was performed and data were further analyzed using a support vector machine (SVM). The SVM correctly differentiated between cancer patients and healthy volunteers in 98.8% of cases. The cancer versus non-cancer group patients (healthy volunteers and patients with other lung diseases) were classified correctly by SVM in 87.3% of cases. In the mixed diagnosis groups (only cancer, only COPD, cancer + COPD and control) all 79 out of 79 patients were predicted correctly in the cancer + COPD group, with the rate of correct prognosis in other patient groups being lower. Exhaled breath analysis by electronic nose using a SVM is able to discriminate patients with lung cancer from healthy subjects and mixed groups of patients with different lung diseases. It can also provide a certain level of discrimination between lung cancer patients, lung cancer patients with concomitant COPD, COPD alone and a healthy control group.

Background and Objective. Chronic obstructive pulmonary disease (COPD) is characterized by a persistence of inflammation in large and small airways. We hypothesized that this could be caused by the inability of an inflammatory process to resolve. In the resolution of inflammation, a switching of arachidonic acid metabolism from the production of proinflammatory leukotriene B4 (LtB4) to the synthesis of anti-inflammatory lipoxins plays an important role. The aim of our study was to determine the content of lipoxin A4 (LXA4) and LtB4 in induced sputum of patients with exacerbated COPD and to compare it to healthy controls, as well as to analyze the relationship between proinflammatory and anti-inflammatory mediators and an inflammatory cell spectrum in induced sputum. Material and Methods. Induced sputum from 17 COPD patients and 7 healthy controls were analyzed for LXA4 and LtB4 content and inflammatory cell spectrum. Results. COPD patients had a significantly lower sputum LXA4 concentration and LtB4/LXA4 ratio compared with healthy controls. A significant negative correlation was found between the LXA4 concentration and the relative neutrophil count and between the LtB4/LXA4 ratio and the relative macrophage count. Conclusions. COPD patients during the late phase of exacerbation had a suppressed production of LXA4 and an elevated LtB4/LXA4 ratio in induced sputum demonstrating a proinflammatory imbalance. The correction of a balance between proinflammatory and anti-inflammatory eicosanoids by the administration of stable analogues of lipoxins could improve the treatment of chronic obstructive pulmonary disease in the future.

Early diagnosis of lung cancer is important due to high mortality in late stages of the disease. An ideal approach for population screening could be the breath analysis, due to its non-invasiveness, simplicity and cheapness. Using sensitive methods of analysis like gas chromatography/mass spectrometry in exhaled air of cancer patients were discovered some volatile organic compounds – possible candidates for cancer markers. However, these compounds were not specific for cancer cells. At the same time, integrative approaches used to analyze the exhaled breath have demonstrated high sensitivity and specificity of this method for lung cancer diagnosis. Such integrative approaches include detection of smell prints by electronic nose or integrated analysis of wide range of volatile organic compounds detected by gas chromatography/mass spectrometry or related methods. Modern statistical pattern recognition systems like logistic regression analysis, support vector machine or analysis by artificial neuronal network may improve diagnostic accuracy

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In the paper, the authors analyze the preliminary results of testing a classical gas sensing instrument -the electronic nose (a metal oxide transistor sensor of chemical substances) in a hospital where patients with different lung diseases are treated. To reveal the correlation between the amplitudes of the sensor's responses and the patients' diagnoses, different statistical analysis methods have been used. It is shown that the lung cancer can easily be discriminated from other lung diseases if short breath sampling and analysis time (less than 1 min) is used in the test. Volatiles obtained from a breath sample of a patient with lung cancer give the major contribution to the responses of different e-nose sensors, so in these cases highly precise identification could be achieved.

This study demonstrates that young smokers have early inflammatory changes in their airways that not only initiate nonspecific mechanisms recruiting neutrophils, but also involve specific immune mechanisms with recruitment of T regulatory lymphocytes. The lymphocyte response is probably adaptive.

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile and perfume industry as well as for air pollution control. The aim of this study was to test whether exhaled breath analysed by an artificial nose could identify and discriminate between different lung diseases. A total of 76 individuals were tested: 25 bronchial asthma, 19 lung cancer, 10 pneumonia, 6 chronic obstructive pulmonary disease (COPD) patients and 16 healthy volunteers. Exhaled air was collected in plastic bags and immediately analysed using an electronic nose instrument (9185, Nordic Sensors AB) containing 14 different odour sensors. Multifactor logistic regression analysis was used to determine correlation between the amplitudes of sensor responses and the clinical diagnoses of patients and to calculate sensitivity and specificity of the method for each diagnosis. For diagnostics of asthma the sensitivity was found to be 84% and specificity — 86%. For lung cancer, the sensitivity was 74% and specificity, 95%; for pneumonia 90% and 98%, but for COPD, 33% and 97%, respectively. We conclude that an artificial nose is able to discriminate among different lung diseases with sufficiently good accuracy. This method may be further developed to implement it in clinical medicine for express diagnostics of acute and chronic lung diseases.