Cancerous glucose metabolism in lung cancer—evidence from exhaled breath analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 026012 ◽  
Author(s):  
Tali Feinberg ◽  
Layah Alkoby-Meshulam ◽  
Jens Herbig ◽  
John C Cancilla ◽  
Jose S Torrecilla ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Glucose Metabolism ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

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

Chemosensors ◽  
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.

scholarly journals Exhaled Breath Analysis for Monitoring Response to Treatment in Advanced Lung Cancer

2016 ◽  
Vol 11 (6) ◽  
pp. 827-837 ◽  
Author(s):  
Inbar Nardi-Agmon ◽  
Manal Abud-Hawa ◽  
Ori Liran ◽  
Naomi Gai-Mor ◽  
Maya Ilouze ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Breath Analysis ◽  
Response To Treatment ◽  
Advanced Lung Cancer ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Monitoring Response

scholarly journals An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer

2021 ◽  
Vol Volume 12 ◽  
pp. 81-92
Author(s):  
Nir Peled ◽  
Vered Fuchs ◽  
Emily H Kestenbaum ◽  
Elron Oscar ◽  
Raul Bitran
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

scholarly journals COMBINED DIAGNOSTICS OF LUNG CANCER USING EXHALED BREATH ANALSYSIS AND SPUTUM CYTOLOGY

2020 ◽  
Vol 66 (4) ◽  
pp. 381-384
Author(s):  
A. Arseniev ◽  
A. Nefedova ◽  
A. Ganeeva ◽  
A. Nefedov ◽  
S. Novikov ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Breath Test ◽  
Early Stage ◽  
Breath Analysis ◽  
Diagnostic Techniques ◽  
Cancer Diagnostics ◽  
Exhaled Breath ◽  
Sputum Cytology ◽  
Exhaled Breath Analysis ◽  
Sensitivity Specificity

In this article we summarize our own experience of lung cancer diagnostics using exhaled breath analysis with a non-selective method using metal oxide chemoresistor gas sensors with cross-sensitivity combined with the sputum cytology. Volatile organic compounds of exhaled breath change the conductivity of the sensor, the resulting pulse is displayed as a peak on the graph, the area of which is used as test results. The combination of two diagnostic techniques in 204 participants demonstrated the possibility of non-invasively detecting the disease at an early stage. The sensitivity, specificity and accuracy of the breath analysis was 91.2%, 100% and 93.4%, respectively. The combination of the breath test and the sputum cytology compared to the breath test alone showed statistically significant (p = 0.03) increase in sensitivity to 96.8% (95% CI: 80.9% -99%) with acceptable decrease in specificity to 93.4% (95% CI: 88% -96%). The convenience of analysis and realtime measurements show some promise for the early detection.

scholarly journals VOC biomarkers identification and predictive model construction for lung cancer based on exhaled breath analysis: research protocol for an exploratory study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028448 ◽  
Author(s):  
Wenwen Li ◽  
Wei Dai ◽  
Mingxin Liu ◽  
Yijing Long ◽  
Chunyan Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Predictive Model ◽  
Cancer Patients ◽  
Healthy Subjects ◽  
Solid Phase ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Lung Cancer Patients ◽  
Exhaled Breath Analysis

IntroductionLung cancer is the most common cancer and the leading cause of cancer death in China, as well as in the world. Late diagnosis is the main obstacle to improving survival. Currently, early detection methods for lung cancer have many limitations, for example, low specificity, risk of radiation exposure and overdiagnosis. Exhaled breath analysis is one of the most promising non-invasive techniques for early detection of lung cancer. The aim of this study is to identify volatile organic compound (VOC) biomarkers in lung cancer and to construct a predictive model for lung cancer based on exhaled breath analysis.Methods and analysisThe study will recruit 389 lung cancer patients in one cancer centre and 389 healthy subjects in two lung cancer screening centres. Bio-VOC breath sampler and Tedlar bag will be used to collect breath samples. Gas chromatography-mass spectrometry coupled with solid phase microextraction technique will be used to analyse VOCs in exhaled breath. VOC biomarkers with statistical significance and showing abilities to discriminate lung cancer patients from healthy subjects will be selected for the construction of predictive model for lung cancer.Ethics and disseminationThe study was approved by the Ethics Committee of Sichuan Cancer Hospital on 6 April 2017 (No. SCCHEC-02-2017-011). The results of this study will be disseminated in presentations at academic conferences, publications in peer-reviewed journals and the news media.Trial registration numberChiCTR-DOD-17011134; Pre-results.

Combining exhaled-breath analysis data with clinical parameters to improve the diagnosis of lung cancer

2019 ◽  
Author(s):  
Sharina Kort ◽  
Marjolein Brusse-Keizer ◽  
Hugo Schouwink ◽  
Emanuel Citgez ◽  
Frans De Jongh ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Breath Analysis ◽  
Analysis Data ◽  
Clinical Parameters ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Diagnosis Of Lung Cancer

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

2020 ◽  
Vol 6 (1) ◽  
pp. 00221-2019 ◽  
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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