Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples

• Published in: PloS one Vol. 17; no. 11; p. e0277431
• Main Authors: Rai, Shesh N., Das, Samarendra, Pan, Jianmin, Mishra, Dwijesh C., Fu, Xiao-An
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.11.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Body Fluids; Breath tests; Cancer research; Carbonyl compounds; Carbonyls; Classification; Computer and Information Sciences; COVID-19; Diagnosis; Fatalities; Health aspects; Humans; Identification and classification; Learning algorithms; Low income groups; Lung cancer; Lung diseases; Lung Neoplasms - diagnosis; Lung nodules; Machine learning; Medical diagnosis; Medicine and Health Sciences; Metabolism; Microreactors; Multiple Pulmonary Nodules; Nodules; Organic chemicals; Organic compounds; Patients; Physical Sciences; Samples; Silicon; Smoking; Standard error; Statistical analysis; Survival; Tomography; VOCs; Volatile Organic Compounds; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277431

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C 4 H 8 O 2 , C 13 H 22 O, C 11 H 22 O, C 2 H 4 O 2 , C 7 H 14 O, C 6 H 12 O, and C 5 H 8 O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.