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- W4313017628 abstract "<b>Introduction:</b> Monitoring of volatile organic compounds (VOCs) in exhaled breath has been used for lung cancer (LC) diagnosis in recent years. However, a standardized VOC analysis method for candidate VOCs as LC biomarkers has not been developed yet. <b>Aims and objectives:</b> To describe a new strategy for LC diagnosis by fingerprinting VOCs in exhaled breath (EB) using selected ion monitoring (SIM) mode and machine learning algorithms. <b>Methods:</b> In this study, 70 patients with LC and 96 controls without LC were included. Their fresh exhaled breath samples were sampled by lab-made polythiophene solid-phase microextraction (SPME) fiber under dynamic conditions for the VOCs detection. After the sampling process, adsorbed VOCs on fiber were analyzed by gas chromatography-mass spectrometry (GC-MS) using SIM mode between 13-94 m/z. Specific patterns of VOC biomarkers in each m/z chromatogram were found by comparing relevant characteristic retention times (RT) and its areas between LC and control. The performance of each VOC as a biomarker in labeling LC and control individuals was performed using machine learning algorithms. <b>Results:</b> Performance of the model for differentiation LC from control with accuracy, AUC and F Score values were 0.818, 0.816 and 0.817, respectively. <b>Conclusion:</b> The selected ion tracking mode, supported by machine learning algorithms, can successfully differentiate LC patients from non-cancer individuals. <b>Acknowledgments:</b> We thank TUBITAK (113Z672) and T.C. Presidency of Strategy and Budget (2019K12-149080) for their grant supports. The patent-pending of this study was made to the Turkish Patent Institute (# 2021/011862)" @default.
- W4313017628 created "2023-01-05" @default.
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- W4313017628 date "2022-09-04" @default.
- W4313017628 modified "2023-09-27" @default.
- W4313017628 title "A novel method exhale breath fingerprinting for lung cancer diagnosis by selected ion monitoring mode" @default.
- W4313017628 doi "https://doi.org/10.1183/13993003.congress-2022.1515" @default.
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