The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated

Early detection of lung cancer usually markedly increases the efficiency of therapy. However, the currently employed diagnostic approaches are not sufficiently effective, resulting in late detection of the disease and high patient mortality. Therefore, development of a high-throughput and reliable diagnostic method is a priority task requiring fast solution. Analysis of exhaled air for a number of organic compounds recognized as lung cancer biomarkers seems to be a promising approach for early diagnosis of the disease. This issue attracts growing interest, as indicated by increasing number of publications on this topic. This review surveys contemporary analytical techniques for analysis of exhaled air, including various spectroscopic and mass spectral methods and also gas sensor-based methods. The key benefits and shortcomings of the techniques, sample injection and pre-concentration methods, and the potential applicability of the methods for lung cancer detection are discussed. The prospects of simultaneous application of several analytical techniques and approaches for the early diagnosis are demonstrated. The bibliography includes 147 references.

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In this article we summarize our own experience of lung cancer diagnostics using exhaled breath analysis with a non-selective method using metal oxide chemoresistor gas sensors with cross-sensitivity combined with the sputum cytology. Volatile organic compounds of exhaled breath change the conductivity of the sensor, the resulting pulse is displayed as a peak on the graph, the area of which is used as test results. The combination of two diagnostic techniques in 204 participants demonstrated the possibility of non-invasively detecting the disease at an early stage. The sensitivity, specificity and accuracy of the breath analysis was 91.2%, 100% and 93.4%, respectively. The combination of the breath test and the sputum cytology compared to the breath test alone showed statistically significant (p = 0.03) increase in sensitivity to 96.8% (95% CI: 80.9% -99%) with acceptable decrease in specificity to 93.4% (95% CI: 88% -96%). The convenience of analysis and realtime measurements show some promise for the early detection.