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Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. / Kononov, Aleksandr; Korotetsky, Boris; Jahatspanian, Igor; Gubal, Anna; Vasiliev, Alexey; Arsenjev, Andrey; Nefedov, Andrey; Barchuk, Anton; Gorbunov, Ilya; Kozyrev, Kirill; Rassadina, Anna; Iakovleva, Evgenia; Sillanpaä, Mika; Safaei, Zahra; Ivanenko, Natalya; Stolyarova, Nadezhda; Chuchina, Victoria; Ganeev, Alexandr.

в: Journal of Breath Research, Том 14, № 1, 016004, 01.2020.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

Harvard

Kononov, A, Korotetsky, B, Jahatspanian, I, Gubal, A, Vasiliev, A, Arsenjev, A, Nefedov, A, Barchuk, A, Gorbunov, I, Kozyrev, K, Rassadina, A, Iakovleva, E, Sillanpaä, M, Safaei, Z, Ivanenko, N, Stolyarova, N, Chuchina, V & Ganeev, A 2020, 'Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer', Journal of Breath Research, Том. 14, № 1, 016004. https://doi.org/10.1088/1752-7163/ab433d, https://doi.org/10.1088/1752-7163/ab433d

APA

Kononov, A., Korotetsky, B., Jahatspanian, I., Gubal, A., Vasiliev, A., Arsenjev, A., Nefedov, A., Barchuk, A., Gorbunov, I., Kozyrev, K., Rassadina, A., Iakovleva, E., Sillanpaä, M., Safaei, Z., Ivanenko, N., Stolyarova, N., Chuchina, V., & Ganeev, A. (2020). Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. Journal of Breath Research, 14(1), [016004]. https://doi.org/10.1088/1752-7163/ab433d, https://doi.org/10.1088/1752-7163/ab433d

Vancouver

Author

Kononov, Aleksandr ; Korotetsky, Boris ; Jahatspanian, Igor ; Gubal, Anna ; Vasiliev, Alexey ; Arsenjev, Andrey ; Nefedov, Andrey ; Barchuk, Anton ; Gorbunov, Ilya ; Kozyrev, Kirill ; Rassadina, Anna ; Iakovleva, Evgenia ; Sillanpaä, Mika ; Safaei, Zahra ; Ivanenko, Natalya ; Stolyarova, Nadezhda ; Chuchina, Victoria ; Ganeev, Alexandr. / Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. в: Journal of Breath Research. 2020 ; Том 14, № 1.

BibTeX

@article{8aaf6f2471a34b9d8948ab267fa4f66b,
title = "Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer",
abstract = "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.",
keywords = "breath analysis, early diagnostics, electronic nose, lung cancer, metal oxide sensors, volatile organic compounds, ARRAY, IMPLEMENTATION, SENSITIVITY, CLASSIFICATION, AIR, IDENTIFICATION, BIOMARKERS, ISSUES, ORIGIN, EXHALED BREATH, Metals/chemistry, Humans, Middle Aged, Lung Neoplasms/diagnosis, Electric Conductivity, Case-Control Studies, Electronic Nose, Breath Tests/methods, Semiconductors, Logistic Models, Calibration, Oxides/chemistry, Algorithms, ROC Curve, Aged, Exhalation, Internet",
author = "Aleksandr Kononov and Boris Korotetsky and Igor Jahatspanian and Anna Gubal and Alexey Vasiliev and Andrey Arsenjev and Andrey Nefedov and Anton Barchuk and Ilya Gorbunov and Kirill Kozyrev and Anna Rassadina and Evgenia Iakovleva and Mika Sillanpa{\"a} and Zahra Safaei and Natalya Ivanenko and Nadezhda Stolyarova and Victoria Chuchina and Alexandr Ganeev",
year = "2020",
month = jan,
doi = "https://doi.org/10.1088/1752-7163/ab433d",
language = "English",
volume = "14",
journal = "Journal of Breath Research",
issn = "1752-7155",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

AU - Kononov, Aleksandr

AU - Korotetsky, Boris

AU - Jahatspanian, Igor

AU - Gubal, Anna

AU - Vasiliev, Alexey

AU - Arsenjev, Andrey

AU - Nefedov, Andrey

AU - Barchuk, Anton

AU - Gorbunov, Ilya

AU - Kozyrev, Kirill

AU - Rassadina, Anna

AU - Iakovleva, Evgenia

AU - Sillanpaä, Mika

AU - Safaei, Zahra

AU - Ivanenko, Natalya

AU - Stolyarova, Nadezhda

AU - Chuchina, Victoria

AU - Ganeev, Alexandr

PY - 2020/1

Y1 - 2020/1

N2 - 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.

AB - 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.

KW - breath analysis

KW - early diagnostics

KW - electronic nose

KW - lung cancer

KW - metal oxide sensors

KW - volatile organic compounds

KW - ARRAY

KW - IMPLEMENTATION

KW - SENSITIVITY

KW - CLASSIFICATION

KW - AIR

KW - IDENTIFICATION

KW - BIOMARKERS

KW - ISSUES

KW - ORIGIN

KW - EXHALED BREATH

KW - Metals/chemistry

KW - Humans

KW - Middle Aged

KW - Lung Neoplasms/diagnosis

KW - Electric Conductivity

KW - Case-Control Studies

KW - Electronic Nose

KW - Breath Tests/methods

KW - Semiconductors

KW - Logistic Models

KW - Calibration

KW - Oxides/chemistry

KW - Algorithms

KW - ROC Curve

KW - Aged

KW - Exhalation

KW - Internet

UR - http://www.scopus.com/inward/record.url?scp=85073764300&partnerID=8YFLogxK

U2 - https://doi.org/10.1088/1752-7163/ab433d

DO - https://doi.org/10.1088/1752-7163/ab433d

M3 - Article

C2 - 31505480

AN - SCOPUS:85073764300

VL - 14

JO - Journal of Breath Research

JF - Journal of Breath Research

SN - 1752-7155

IS - 1

M1 - 016004

ER -

ID: 47856911