Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose

This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung...

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Veröffentlicht in:Expert review of molecular diagnostics Jg. 21; H. 11; S. 1223 - 1233
Hauptverfasser: V A, Binson, Subramoniam, M., Mathew, Luke
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Taylor & Francis 02.11.2021
Schlagworte:
breath analysis
Breath Tests - methods
COPD
Electronic Nose
Humans
lung cancer
Lung Neoplasms - diagnosis
Pulmonary Disease, Chronic Obstructive - diagnosis
volatile organic compounds
Volatile Organic Compounds - analysis
lung cancer
electronic nose
breath analysis
volatile organic compounds
COPD
ISSN:1473-7159, 1744-8352, 1744-8352
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Zusammenfassung:This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls. This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls. In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy. The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1473-7159
1744-8352
1744-8352
DOI:10.1080/14737159.2021.1971079