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

Lung Cancer. 2019 Sep:135:123-129. doi: 10.1016/j.lungcan.2019.02.012. Epub 2019 Feb 15.

Abstract

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.

Keywords: Artificial neural network; Breath samples; Discriminant function analysis; Lung cancer; Statistical analysis; Volatile organic compounds.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers, Tumor*
  • Breath Tests
  • Discriminant Analysis
  • Exhalation*
  • Female
  • Gas Chromatography-Mass Spectrometry
  • Humans
  • Lung Neoplasms / diagnosis*
  • Lung Neoplasms / metabolism*
  • Male
  • Middle Aged
  • Neural Networks, Computer
  • ROC Curve
  • Volatile Organic Compounds / analysis*
  • Volatile Organic Compounds / metabolism

Substances

  • Biomarkers, Tumor
  • Volatile Organic Compounds