Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

Aleksandr Kononov, Boris Korotetsky, Igor Jahatspanian, Anna Gubal, Alexey Vasiliev, Andrey Arsenjev, Andrey Nefedov, Anton Barchuk, Ilya Gorbunov, Kirill Kozyrev, Anna Rassadina, Evgenia Iakovleva, Mika Sillanpaä, Zahra Safaei, Natalya Ivanenko, Nadezhda Stolyarova, Victoria Chuchina, Alexandr Ganeev

Результат исследований: Научные публикации в периодических изданияхстатья

5 Цитирования (Scopus)

Аннотация

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 March 15, 2017.

Язык оригиналаанглийский
Номер статьи016004
Число страниц14
ЖурналJournal of Breath Research
Том14
Номер выпуска1
Ранняя дата в режиме онлайн23 окт 2019
DOI
СостояниеОпубликовано - янв 2020

Предметные области Scopus

  • Пульмонология и респираторная медицина

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