| Title: | Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study |
| Author(s): | Mentel S; Gallo K; Wagendorf O; Preissner R; Nahles S; Heiland M; Preissner S; |
| Address: | "Department Oral and Maxillofacial Surgery, Charite - Universitatsmedizin Berlin, Corporate Member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany. Science-IT and Institute of Physiology, Charite - Universitatsmedizin Berlin, Corporate Member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115, Berlin, Germany. Department Oral and Maxillofacial Surgery, Charite - Universitatsmedizin Berlin, Corporate Member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany. saskia.preissner@charite.de" |
| DOI: | 10.1186/s12903-021-01862-z |
| ISSN/ISBN: | 1472-6831 (Electronic) 1472-6831 (Linking) |
| Abstract: | "BACKGROUND: The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC). METHODS: Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning. RESULTS: Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86-90%. CONCLUSIONS: Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles" |
| Keywords: | "*Carcinoma, Squamous Cell/diagnosis *Head and Neck Neoplasms Humans Machine Learning *Mouth Neoplasms/diagnosis Prospective Studies Squamous Cell Carcinoma of Head and Neck Breath analysis Gas chromatography-ion mass spectrometry Head and neck cancer Oral;" |
| Notes: | "MedlineMentel, Sophia Gallo, Kathleen Wagendorf, Oliver Preissner, Robert Nahles, Susanne Heiland, Max Preissner, Saskia eng Research Support, Non-U.S. Gov't England 2021/10/08 BMC Oral Health. 2021 Oct 6; 21(1):500. doi: 10.1186/s12903-021-01862-z" |
