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

Nuwan Dharmawardana; Thomas Goddard; Charmaine Woods; David I. Watson; Eng H. Ooi; Roger Yazbeck

doi:10.1038/s41416-020-01051-9

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

Nuwan Dharmawardana, Thomas Goddard, Charmaine Woods, David I. Watson, Eng H. Ooi, Roger Yazbeck

College of Medicine and Public Health

Research output: Contribution to journal › Article › peer-review

23 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	1775-1781
Number of pages	7
Journal	British Journal of Cancer
Volume	123
Issue number	12
Early online date	9 Sept 2020
DOIs	https://doi.org/10.1038/s41416-020-01051-9
Publication status	Published - 8 Dec 2020

Bibliographical note

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).

Keywords

Diagnostic markers
Head and neck cancer
Translational research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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s41416-020-01051-9Final published version, 496 KBLicence: CC BY

Cite this

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title = "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.",

keywords = "Diagnostic markers, Head and neck cancer, Translational research",

author = "Nuwan Dharmawardana and Thomas Goddard and Charmaine Woods and Watson, {David I.} and Ooi, {Eng H.} and Roger Yazbeck",

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AU - Goddard, Thomas

AU - Woods, Charmaine

AU - Watson, David I.

AU - Ooi, Eng H.

AU - Yazbeck, Roger

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N2 - 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.

AB - 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.

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

Abstract

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Keywords

UN SDGs

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