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BMC Oral Health
Published in: BMC Oral Health 1/2021

Open Access 01-12-2021 | Oral Cancer | Research

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Authors: Sophia Mentel, Kathleen Gallo, Oliver Wagendorf, Robert Preissner, Susanne Nahles, Max Heiland, Saskia Preissner

Published in: BMC Oral Health | Issue 1/2021

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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.
Appendix
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Metadata
Title
Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study
Authors
Sophia Mentel
Kathleen Gallo
Oliver Wagendorf
Robert Preissner
Susanne Nahles
Max Heiland
Saskia Preissner
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2021
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-021-01862-z

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