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Abstract
Introduction. Exhaled breath analysis represents a promising non-invasive approach for diagnosing laryngeal, oral cavity, and pharyngeal cancers. This method is based on the premise that cancer cell metabolism differs from healthy cells, resulting in the production of specific volatile organic compounds.
Aim. To evaluate the diagnostic performance of a multisensor gas analysis system combined with an artificial neural network for detecting upper respiratory tract cancer at early (I-II) and advanced (III-IV) stages using exhaled breath samples.
Material and Methods. The study included 78 patients with oral cavity, laryngeal, and hypopharyngeal cancers, along with 47 healthy volunteers. Breath analysis was performed using a custom-developed diagnostic device that detects volatile compounds through an array of semiconductor sensors, with subsequent data processing by a neural network.
Results. All experiments were conducted using exhaled air. The neural network demonstrated high performance in distinguishing cancer patients from healthy volunteers (n = 30 patients vs 35 controls), achieving 93 % sensitivity and 83 % specificity. For staging classification between early (I-II) and advanced (III-IV) disease, the system achieved 81 % sensitivity and 58 % specificity, with an overall accuracy of 69 %.
Conclusion. The developed non-invasive method achieved 88 % diagnostic accuracy for detecting upper respiratory tract cancer across all stages (I-IV) using exhaled breath samples. The system demonstrated 69 % accuracy in specifically identifying early-stage (I-II) cancers within the full patient cohort.
References
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