The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Autor:	Poļaka I; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Department of Modelling and Simulation, Riga Technical University, LV-1048 Riga, Latvia., Mežmale L; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia.; Riga East University Hospital, LV-1038 Riga, Latvia.; Faculty of Residency, Riga Stradins University, LV-1007 Riga, Latvia.; Health Centre 4, LV-1012 Riga, Latvia., Anarkulova L; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Faculty of Residency, Riga Stradins University, LV-1007 Riga, Latvia.; Health Centre 4, LV-1012 Riga, Latvia.; Liepaja Regional Hospital, LV-3414 Liepaja, Latvia., Kononova E; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Faculty of Medicine, Riga Stradins University, LV-1007 Riga, Latvia., Vilkoite I; Health Centre 4, LV-1012 Riga, Latvia.; Department of Doctoral Studies, Riga Stradins University, LV-1007 Riga, Latvia., Veliks V; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia., Ļeščinska AM; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Riga East University Hospital, LV-1038 Riga, Latvia.; Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia., Stonāns I; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia., Pčolkins A; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia.; Riga East University Hospital, LV-1038 Riga, Latvia., Tolmanis I; Faculty of Medicine, Riga Stradins University, LV-1007 Riga, Latvia.; Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia., Shani G; Laboratory for Nanomaterial-Based Devices, Technion-Israel Institute of Technology, Haifa 3200003, Israel., Haick H; Laboratory for Nanomaterial-Based Devices, Technion-Israel Institute of Technology, Haifa 3200003, Israel., Mitrovics J; JLM Innovation GmbH, D-72070 Tübingen, Germany., Glöckler J; Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany., Mizaikoff B; Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany.; Hahn-Schickard, 89077 Ulm, Germany., Leja M; Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.; Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia.; Riga East University Hospital, LV-1038 Riga, Latvia.; Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia.
Jazyk:	angličtina
Zdroj:	Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2023 Oct 31; Vol. 13 (21). Date of Electronic Publication: 2023 Oct 31.
DOI:	10.3390/diagnostics13213355
Abstrakt:	Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.
Databáze:	MEDLINE
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