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. 2022 Aug 29;22(17):6485.
doi: 10.3390/s22176485.

The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath

Roberta Anzivino  1 , Pasqua Irene Sciancalepore  2 , Silvano Dragonieri  3 , Vitaliano Nicola Quaranta  3 , Paolo Petrone  4 , Domenico Petrone  1 , Nicola Quaranta  5 , Giovanna Elisiana Carpagnano  3
Affiliations

The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath

Roberta Anzivino et al. Sensors (Basel). .

Abstract

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool.

Keywords: e-nose; electronic nose; exhaled breath; head and neck cancer; volatile organic compounds.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Setup for breath collection. From left to right: A2-environmental VOCs filter; 2-way non rebreathing valve; expiratory port; tedlar bag.
Figure 2
Figure 2
Working principle of Cyranose 320: it is based on a nano-composite array of 32 organic polymer sensors. If exposed to VOC combinations, the polymers swell, thereby modifying their electrical resistance. Raw data are registered as the increase in resistance of any single sensors and the combination of all signals results in a “breathprint”.
Figure 3
Figure 3
Two-dimensional PCA with 2 composite factors (x axis = Principal Component 3; y axis = Principal Component 1) showing the discrimination between patients with HNC (blue circles), allergic rhinitis (red squares) and healthy controls (green triangles). Cross-validated accuracy was 75.3% (p < 0.01).
Figure 4
Figure 4
ROC-curve with line of identity of the breathprint discriminant function (representing PC1 and PC3), predictive for the discrimination of HNC from allergic rhinitis and healthy controls. AUC was 0.871 (95% CI: 0.749–0.994). Blue line = ROC-curve; Red line = random classifier.
See this image and copyright information in PMC

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