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Review
. 2019 Jun 26;28(152):190011.
doi: 10.1183/16000617.0011-2019. Print 2019 Jun 30.

Sensors for detecting pulmonary diseases from exhaled breath

Dina Hashoul  1 , Hossam Haick  2
Affiliations
Review

Sensors for detecting pulmonary diseases from exhaled breath

Dina Hashoul et al. Eur Respir Rev. .

Abstract

This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath. One part of the review is a didactic presentation of the overlaying concept and the chemistry of exhaled breath. The other part discusses diverse sensors that have been developed and used for the detection of respiratory diseases (e.g. chronic obstructive pulmonary disease, asthma, lung cancer, pulmonary arterial hypertension, tuberculosis, cystic fibrosis, obstructive sleep apnoea syndrome and pneumoconiosis) by analysis of VOCs in exhaled breath. The strengths and pitfalls are discussed and criticised, particularly in the perspective in disseminating information regarding these advances. Ideas regarding the improvement of sensors, sensor arrays, sensing devices and the further planning of workflow are also discussed.

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Conflict of interest statement

Conflict of interest: D. Hashoul has nothing to disclose. Conflict of interest: H. Haick has nothing to disclose.

Figures

FIGURE 1
FIGURE 1
a) Overview of the working principal of nanomaterial-based sensors array. b) Different nanomaterial-based sensors. 1) Chemiresistor based on monolayer-capped nanoparticles; 2) chemiresistors based on single-wall carbon nanotubes; 3) chemiresistor based on conducting polymers; 4) chemiresistor based on metal oxide film; 5) quartz microbalance with selective coating; 6) colorimetric sensor; and 7) surface acoustic wave sensor. Reproduced from [11] with permission from the publisher.
FIGURE 2
FIGURE 2
Discriminant factor analysis plots calculated from the responses of the nanoarray sensors for a) early lung cancer and benign pulmonary nodules and b) patients with lung cancer with and without the epidermal growth factor receptor (EGFR) mutation. Each point represents one patient. The positions of the mean values are marked with an unfilled square; the boxes correspond to the first and third quartiles, and the error bars correspond to the sd. CV1: first canonical variable. Reproduced from [36] with permission from the publisher.
FIGURE 3
FIGURE 3
a) Dot plots response of a single molecularly modified gold nanoparticle. Each symbol represents a single sample. The dashed line represents Youden's cut-off point, and the dotted lines represent the cut-off points to rule in and rule out tuberculosis. Samples from the validation set with responses lower than the threshold were classified as tuberculosis positive (open stars) or nontuberculosis positive (closed stars) according to Youden's cut-off point. b) Receiver operating characteristic curve of the sensor. c) Receiver operating characteristic curves of the sensor when comparing smoking, HIV and treatment status. AUC: area under curve. Reproduced from [56] with permission from the publisher.
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