Development of a non-invasive exhaled breath test for the diagnosis of head and neck cancer

Abstract

Background: Improving the ability to identify early-stage head and neck squamous cell carcinoma (HNSCC) can improve treatment outcomes and patient morbidity. We sought to determine the diagnostic accuracy of breath analysis as a non-invasive test for detecting HNSCC.

Methods: Standardised breath samples were collected from 181 patients suspected of HNSCC prior to any treatment. A selected ion flow-tube mass spectrometer was used to analyse breath for volatile organic compounds. Diagnosis was confirmed by histopathology. A binomial logistic regression model was used to differentiate breath profiles between cancer and control (benign disease) patients based on mass spectrometry derived variables.

Results: In all, 66% of participants had early-stage primary tumours (T1 and T2) and 58% had regional node metastasis. The optimised logistic regression model using three variables had a sensitivity and specificity of 80% and 86%, respectively, with an AUC for ROC curve of 0.821 (95%CI 0.625-1.0) in the testing cohort.

Conclusions: Breath analysis for non-invasive diagnosis of HNSCC appears to be practical and accurate. Future studies should be conducted in a primary care setting to determine the applicability of breath analysis for early identification of HNSCC.

Fig. 1

Data analysis pipeline for case selection, data optimisation, training/testing data split, dimension reduction, model building and model testing.

Fig. 2

Model 1—Receiver operating curve of the testing cohort based on logistic regression model using the two variables from distinct reagent ions.

Fig. 3

Model 2—Receiver operating curve of the testing cohort based on logistic regression model with one variable from each reagent ion (three variables in total).