Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca−). Exhaled breath samples from 49 Ca+ patients, 36 Ca− patients and 52 healthy controls (HC) were analyzed by an SPME–GC–MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca− patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca− patients, which was not achieved by the targeted approach.

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

Journal of Breath Research ◽

10.1088/1752-7155/10/2/026012 ◽

2016 ◽

Vol 10 (2) ◽

pp. 026012 ◽

Cited By ~ 15

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

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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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Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

Metabolites ◽

10.3390/metabo10080317 ◽

2020 ◽

Vol 10 (8) ◽

pp. 317

Author(s):

Michalis Koureas ◽

Paraskevi Kirgou ◽

Grigoris Amoutzias ◽

Christos Hadjichristodoulou ◽

Konstantinos Gourgoulianis ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Lung Cancer ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Ambient Air ◽

Breath Analysis ◽

Exhaled Breath ◽

Healthy Controls ◽

Volatile Organic

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

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ePS6.01 Targeted analysis of volatile organic compounds for detection of Pseudomonas aeruginosa in cystic fibrosis patients by exhaled breath analysis

Journal of Cystic Fibrosis ◽

10.1016/s1569-1993(20)30329-5 ◽

2020 ◽

Vol 19 ◽

pp. S52

Author(s):

R. Kos ◽

P. Brinkman ◽

A.H. Neerincx ◽

T. Paff ◽

M.G. Gerritsen ◽

...

Keyword(s):

Cystic Fibrosis ◽

Pseudomonas Aeruginosa ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Breath Analysis ◽

Exhaled Breath ◽

Targeted Analysis ◽

Exhaled Breath Analysis ◽

Volatile Organic

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

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2016.02.017 ◽

2016 ◽

Vol 11 (6) ◽

pp. 827-837 ◽

Cited By ~ 43

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

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

Lung Cancer Targets and Therapy ◽

10.2147/lctt.s320493 ◽

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

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

Problems in oncology ◽

10.37469/0507-3758-2020-66-4-381-384 ◽

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.

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

BMJ Open ◽

10.1136/bmjopen-2018-028448 ◽

2019 ◽

Vol 9 (8) ◽

pp. e028448 ◽

Cited By ~ 4

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.

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