Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study

Our plain-English summary of Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study, by K. Lamote, P. Brinkman, L. Vandermeersch, et al (Oncotarget, 2017).

The study

• Authors: K. Lamote, P. Brinkman, L. Vandermeersch, M. Vynck, P.J. Sterk, H. Van Langenhove, O. Thas, J. Van Cleemput, K. Nackaerts, J.P. van Meerbeeck
• Journal: Oncotarget
• Published: 2017;8(53):91593-91602
• Cited by: 54 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Rationale: Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism.

Objectives: The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose).

Methods: Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose.

Results: Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively.

Conclusion: This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.

In plain English

This Belgian multicenter cross-sectional case-control study tested whether breath analysis could identify malignant pleural mesothelioma. It enrolled 64 people: 14 untreated mesothelioma patients, 15 with benign asbestos-related disease (14 with pleural plaques, 1 with asbestosis), 19 symptom-free people formerly exposed to asbestos at work, recruited through a fiber-cement factory's occupational health service, and 16 healthy unexposed controls. Breath was analyzed by gas chromatography-mass spectrometry (GC-MS) and four combined electronic noses. With GC-MS, the 14 mesothelioma patients were separated from the 19 symptom-free exposed people with 97% accuracy (95% confidence interval 86.0% to 99.8%). Against these and the benign-disease patients combined (34 people), accuracy was 94% (84.0% to 98.4%), sensitivity 100% (80.7% to 100%) and specificity 91% (77.9% to 97.7%). Electronic-nose accuracy, based on 50 participants, was lower across mesothelioma comparisons, from 65.2% (44.5% to 82.3%) to 73.7% (58.1% to 85.8%). Neither method could separate the symptom-free exposed people from the benign-disease patients. The authors concluded that the results provide proof-of-principle for breath screening for mesothelioma. They noted small numbers, groups unmatched for age, no data on exposure duration or intensity, possible contamination, and lack of blinding.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Multicenter, cross-sectional, case-control study comparing exhaled breath compounds measured by GC-MS and by electronic nose (eNose)
• Who was studied: 64 participants in Belgium. Three university hospitals recruited 14 treatment-naive malignant pleural mesothelioma (MPM) patients, 15 patients with benign asbestos-related diseases, and 16 healthy non-asbestos-exposed controls. A fiber-cement factory's occupational health service recruited 19 asymptomatic former asbestos-exposed individuals. Among the benign-disease patients, 14 had pleural plaques and 1 had asbestosis. 62 people gave breath samples for GC-MS and 50 for eNose.
• Exposure: Past occupational asbestos exposure, collected by questionnaire. The asymptomatic exposed group was recruited via the occupational health service of a Belgian fiber-cement factory that processed asbestos until 1997. Fiber types, duration and intensity of exposure were not reported.
• Main results:
  • GC-MS, MPM patients (14) vs asymptomatic former asbestos-exposed persons (19): 97% accuracy (86.0%-99.8%), 93% sensitivity, 100% specificity, 100% PPV, 95% NPV; AUC 0.989
  • GC-MS, MPM patients (14) vs exposed persons and benign-disease patients pooled (34): 94% accuracy (84.0%-98.4%), 100% sensitivity, 91% specificity, 82% PPV, 100% NPV; AUC 0.943
  • GC-MS pooled model (MPM vs AEx+ARD): sensitivity 100% with 95% confidence interval 80.7%-100%
  • GC-MS pooled model (MPM vs AEx+ARD): specificity 91.2% with 95% confidence interval 77.9%-97.7%
  • GC-MS, MPM vs benign asbestos-related disease patients: 79% accuracy (61.9%-91.2%). MPM vs healthy controls: 71% accuracy (52.9%-85.7%).
  • The compounds most often selected by the GC-MS models were diethyl ether, limonene, cyclohexane, nonanal, an unidentified compound (VOC IK 1287) and isothiocyanatocyclohexane
  • GC-MS could not separate benign asbestos-related disease patients from asymptomatic exposed persons (50% accuracy, 33.6%-66.4%). The authors used this comparison as a negative control.
  • eNose samples came from 50 participants: 11 MPM, 15 asymptomatic exposed, 12 benign-disease and 12 healthy controls
  • eNose, MPM patients (11) vs healthy controls (12): 65% accuracy (44.5%-82.3%); AUC 0.667
  • eNose, MPM patients (11) vs asymptomatic exposed persons (15): 73% accuracy (53.9%-87.4%); AUC 0.655, which the table does not mark as statistically different from 0.5
  • eNose, MPM patients (11) vs exposed persons and benign-disease patients pooled (27): 74% accuracy (58.1%-85.8%), 82% sensitivity, 55% specificity, 82% PPV, 55% NPV; AUC 0.747
  • eNose could not separate exposed persons from benign-disease patients (52% accuracy, 33.4%-70.0%)
• The authors' conclusion: The authors concluded that GC-MS and eNose distinguished MPM patients from asymptomatic former asbestos-exposed persons with great accuracy. They also concluded that the data provide proof-of-principle for future screening of people at risk, and they said the next step should be a blinded, prospective study.
• Limitations the authors state:
  • Low number of participants
  • Groups not matched for age; a trend toward differences in smoking status
  • Participants were questioned retrospectively, so recall bias is possible. There was no information on the duration or intensity of asbestos exposure.
  • Breath may have been contaminated by outside compounds despite the filter on inhaled air
  • The study was not blinded; samples came from people with known diagnoses
  • The authors attributed the lower eNose results partly to fewer people giving eNose samples

Topics

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Case-Control Study, Prognosis, Latency.

Related studies

• An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls — S. Dragonieri, M.P. van der Schee, et al. (2012)
• Exhaled breath to screen for malignant pleural mesothelioma: a validation study — K. Lamote, M. Vynck, et al. (2017)
• Detection of malignant pleural mesothelioma in exhaled breath by multicapillary column/ion mobility spectrometry (MCC/IMS) — K. Lamote, M. Vynck, et al. (2016)
• A breath test for malignant mesothelioma using an electronic nose — E.A. Chapman, P.S. Thomas, et al. (2012)
• Electronic Nose Technology in Respiratory Diseases — S. Dragonieri, G. Pennazza, et al. (2017)
• Laser ablation inductively coupled plasma mass spectrometry as a novel clinical imaging tool to detect asbestos fibres in malignant mesothelioma — O.M. Voloaca, C.J. Greenhalgh, et al. (2020)

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