A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis

AimsThe aim of this study is to explore DNA hypermethylation analysis in sputum and exhaled breath analysis for their complementary, non-invasive diagnostic capacity in lung cancer.MethodsSputum samples and exhaled breath were prospectively collected from 20 lung cancer patients and 31 COPD controls (Set 1). An additional 18 lung cancer patients and 8 controls only collected exhaled breath as validation set (Set 2). DNA hypermethylation of biomarkers RASSF1A, cytoglobin, APC, FAM19A4, PHACTR3, 3OST2 and PRDM14 was considered, and breathprints from exhaled breath samples were created using an electronic nose (eNose).ResultsBoth DNA hypermethylation markers in sputum and eNose were independently able to distinguish lung cancer patients from controls. The combination of RASSF1A and 3OST2 hypermethylation had a sensitivity of 85% with a specificity of 74%. eNose had a sensitivity of 80% with a specificity of 48%. Sensitivity for lung cancer diagnosis increased to 100%, when RASSF1A hypermethylation was combined with eNose, with specificity of 42%. Both methods showed to be complementary to each other (p≤0.011). eNose results were reproducible in Set 2.ConclusionsWhen used in concert, RASSF1A hypermethylation in sputum and exhaled breath analysis are complementary for lung cancer diagnosis, with 100% sensitivity in this series. This finding should be further validated.

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Improving lung cancer diagnosis from exhaled-breath analysis by adding clinical parameters to the artificial neural network

10.1183/13993003.congress-2019.oa1913 ◽

2019 ◽

Author(s):

Sharina Kort ◽

Marjolein Brusse-Keizer ◽

Hugo Schouwink ◽

Emanuel Citgez ◽

Frans De Jongh ◽

...

Keyword(s):

Neural Network ◽

Lung Cancer ◽

Artificial Neural Network ◽

Cancer Diagnosis ◽

Breath Analysis ◽

Clinical Parameters ◽

Exhaled Breath ◽

Lung Cancer Diagnosis ◽

Exhaled Breath Analysis ◽

Artificial Neural

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MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Chemosensors ◽

10.3390/chemosensors9080209 ◽

2021 ◽

Vol 9 (8) ◽

pp. 209

Author(s):

Davide Marzorati ◽

Luca Mainardi ◽

Giulia Sedda ◽

Roberto Gasparri ◽

Lorenzo Spaggiari ◽

...

Keyword(s):

Lung Cancer ◽

Survival Rate ◽

Mortality Rate ◽

Early Diagnosis ◽

Gas Sensor ◽

Electronic Nose ◽

Gas Sensors ◽

Breath Analysis ◽

Exhaled Breath ◽

Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

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Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

ERJ Open Research ◽

10.1183/23120541.00221-2019 ◽

2020 ◽

Vol 6 (1) ◽

pp. 00221-2019 ◽

Cited By ~ 1

Author(s):

Sharina Kort ◽

Marjolein Brusse-Keizer ◽

Jan Willem Gerritsen ◽

Hugo Schouwink ◽

Emanuel Citgez ◽

...

Keyword(s):

Lung Cancer ◽

Regression Analysis ◽

Clinical Information ◽

Breath Analysis ◽

Multivariate Regression Analysis ◽

Clinical Parameters ◽

Exhaled Breath ◽

Lung Cancer Diagnosis ◽

Exhaled Breath Analysis ◽

Improved Performance

IntroductionExhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis.MethodsBased on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis.ResultsNSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89).ConclusionsAdding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

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Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis

Sensors ◽

10.3390/s20092666 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2666 ◽

Cited By ~ 1

Author(s):

Andrzej Kwiatkowski ◽

Tomasz Chludziński ◽

Tarik Saidi ◽

Tesfalem Geremariam Welearegay ◽

Aylen Lisset Jaimes-Mogollón ◽

...

Keyword(s):

Success Rate ◽

Gas Sensors ◽

Alveolar Echinococcosis ◽

Early Stage ◽

Function Analysis ◽

Breath Analysis ◽

Rapid Identification ◽

Exhaled Breath ◽

Exhaled Breath Analysis ◽

Sensors Array

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO3 nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors’ responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.

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