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Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.
Rai, Shesh N; Das, Samarendra; Pan, Jianmin; Mishra, Dwijesh C; Fu, Xiao-An.
  • Rai SN; Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY, United States of America.
  • Das S; School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, United States of America.
  • Pan J; Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY, United States of America.
  • Mishra DC; Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, United States of America.
  • Fu XA; Biostatistics and Informatics Facility, Center for Integrative Environmental Research Sciences, University of Louisville, Louisville, KY, United States of America.
PLoS One ; 17(11): e0277431, 2022.
Article in English | MEDLINE | ID: covidwho-2140646
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Body Fluids / Multiple Pulmonary Nodules / Volatile Organic Compounds / COVID-19 / Lung Neoplasms Type of study: Diagnostic study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0277431

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Body Fluids / Multiple Pulmonary Nodules / Volatile Organic Compounds / COVID-19 / Lung Neoplasms Type of study: Diagnostic study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0277431