Searching for selected VOCs in human breath samples as potential markers of lung cancer

•Lung cancer is a human health problem.•Analytical methods for detecting potential markers for cancer in breath samples.•Potential markers of cancer detection using statistical methods. Objective: Evaluation of the potential of combined multivariate chemometric methods for seeking markers of lung ca...

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Bibliographic Details
Published in:	Lung cancer (Amsterdam, Netherlands) Netherlands), 2019-09, Vol.135, p.123-129
Main Authors:	Rudnicka, Joanna, Kowalkowski, Tomasz, Buszewski, Bogusław
Format:	Article
Language:	eng
Subjects:	Artificial neural network Breath samples Discriminant function analysis Lung cancer Statistical analysis Volatile organic compounds
Online Access:	Get full text
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Summary:	•Lung cancer is a human health problem.•Analytical methods for detecting potential markers for cancer in breath samples.•Potential markers of cancer detection using statistical methods. Objective: Evaluation of the potential of combined multivariate chemometric methods for seeking markers of lung cancer. Methods: Statistical methods such as Mann-Whitney U test, discriminant function analysis (DFA), factor analysis (FA) and artificial neural network (ANN) were applied to evaluate the obtained data from GC/MS analysis of exhaled breath. Results: The total number of compounds identified by GC/MS in human breath was equal to 88. The statistical analysis indicates seven analytes which have the highest discriminatory power. Cross validation of the obtained model shows that the sensitivity was 80% and the specificity was 91.23%, while for the test group the sensitivity and specificity were both 86.36%. Conclusion: The application of combined statistical methods allowed to reduce the number of compounds to significant ones and indicates them as markers of lung cancer.
ISSN:	0169-5002 1872-8332