Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.
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 thro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277431 |
| _version_ | 1797788225856602112 |
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| author | Shesh N Rai Samarendra Das Jianmin Pan Dwijesh C Mishra Xiao-An Fu |
| author_facet | Shesh N Rai Samarendra Das Jianmin Pan Dwijesh C Mishra Xiao-An Fu |
| author_sort | Shesh N Rai |
| collection | DOAJ |
| description | 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 C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, 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. |
| first_indexed | 2024-03-13T01:32:36Z |
| format | Article |
| id | doaj.art-027c6e5d85bb411ba1a3b7629d8c576a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T01:32:36Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-027c6e5d85bb411ba1a3b7629d8c576a2023-07-04T05:32:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027743110.1371/journal.pone.0277431Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.Shesh N RaiSamarendra DasJianmin PanDwijesh C MishraXiao-An FuEarly 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 C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, 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.https://doi.org/10.1371/journal.pone.0277431 |
| spellingShingle | Shesh N Rai Samarendra Das Jianmin Pan Dwijesh C Mishra Xiao-An Fu Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. PLoS ONE |
| title | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. |
| title_full | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. |
| title_fullStr | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. |
| title_full_unstemmed | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. |
| title_short | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples. |
| title_sort | multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples |
| url | https://doi.org/10.1371/journal.pone.0277431 |
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