ArticleERJ open research2020
Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters.
Article in ERJ open research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- eNose technologies in the detection of cancer: a systematic review and meta-analysis.The oncologist · 2026Pooled it
- Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis.JAMA network open · 2022Pooled it
- Novel biomarkers for lung cancer diagnosis in chronic obstructive pulmonary disease: a systematic review and metanalysis.Discover oncology · 2026Article
- Exploring exhaled volatile organic compounds as potential biomarkers in anti-MDA5 antibody-positive interstitial lung disease.Molecular and cellular biochemistry · 2025Article
- The electronic nose in lung cancer diagnostics: a systematic review and meta-analysis.ERJ open research · 2025Article
- Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study.Molecular diagnosis & therapy · 2024Article
- Is an Electronic Nose Able to Predict Clinical Response following Neoadjuvant Treatment of Rectal Cancer? A Prospective Pilot Study.Journal of clinical medicine · 2024Article
- Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Article
- Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations.International journal of molecular sciences · 2023Article
- Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?Cancers · 2023Review
- Non-invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy.ACS omega · 2022Article
- Application of Artificial Intelligence in Lung Cancer.Cancers · 2022Review
- The promises and challenges of early non-small cell lung cancer detection: patient perceptions, low-dose CT screening, bronchoscopy and biomarkers.Molecular oncology · 2021Review
- The smell of lung disease: a review of the current status of electronic nose technology.Respiratory research · 2021Review
- An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer.Lung Cancer (Auckland, N.Z.) · 2021Review
- Impact of coronavirus disease pandemic on surgery for lung cancer in a provincial city in Japan.Journal of thoracic disease · 2020Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.