Research output: Contribution to journal › Article › peer-review
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.
In: Journal of Breath Research, Vol. 14, No. 1, 016004, 01.2020.Research output: Contribution to journal › Article › peer-review
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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