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. 2016 Nov;273(11):3897-3903.
doi: 10.1007/s00405-016-4038-x. Epub 2016 Apr 16.

Differentiating head and neck carcinoma from lung carcinoma with an electronic nose: a proof of concept study

Affiliations

Differentiating head and neck carcinoma from lung carcinoma with an electronic nose: a proof of concept study

Michel R A van Hooren et al. Eur Arch Otorhinolaryngol. 2016 Nov.

Abstract

Disease specific patterns of volatile organic compounds can be detected in exhaled breath using an electronic nose (e-nose). The aim of this study is to explore whether an e-nose can differentiate between head and neck, and lung carcinoma. Eighty-seven patients received an e-nose measurement before any oncologic treatment. We used PARAFAC/TUCKER3 tensor decomposition for data reduction and an artificial neural network for analysis to obtain binary results; either diagnosed as head and neck or lung carcinoma. Via a leave-one-out method, cross-validation of the data was performed. In differentiating head and neck from lung carcinoma patients, a diagnostic accuracy of 93 % was found. After cross-validation of the data, this resulted in a diagnostic accuracy of 85 %. There seems to be a potential for e-nose as a diagnostic tool in HNC and lung carcinoma. With a fair diagnostic accuracy, an e-nose can differentiate between the two tumor entities.

Keywords: Diagnosis; Electronic nose; Head and neck carcinoma; Lung carcinoma; Volatile organic compounds.

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Conflict of interest statement

The authors of this study did not and will not receive any financial support of companies, associations or organizations. The authors have no other conflicts of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent Informed consent was obtained from all individual participants included in the study.

Figures

Fig. 1
Fig. 1
Scatterplot of best of fit of data
Fig. 2
Fig. 2
Scatterplot of cross-validation of the data
Fig. 3
Fig. 3
Receiver operating characteristic curve

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