SynthesisBMC pulmonary medicine2024
Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review.
Synthesis in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exhaled breath volatile organic compounds (VOCs) detection methods: GC-MS versus eNose in COPD diagnosis-a systematic review and meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Out of the ivory tower: transitioning breathomics to clinical care.ERJ open research · 2026Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
- Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.Cancers · 2025Review
- Machine learning assisted breathomic approach for early-stage thoracic cancer detection.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundThere is a general rise in incidentally found pulmonary nodules (PNs) requiring follow-up due to increased CT use. Biopsy and repeated CT scan are the most useful methods for distinguishing between benign PNs and lung cancer, while they are either invasive or involves radiation exposure. Therefore, there has been increasing interest in the analysis of exhaled volatile organic compounds (VOCs) to distinguish between benign PNs and lung cancer because it's cheap, noninvasive, efficient, and easy-to-use. However, the exact value of breath analysis in this regard remains unclear.
methodsA PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed to include studies that established exhaled VOC-based predictive models to distinguish between benign PNs and lung cancer and reported the exact VOCs used. Data regarding study characteristics, performance of the models, which predictors were incorporated, and methodologies for breath collection and analysis were independently extracted by two researchers. The exhaled VOCs incorporated into the predictive models were narratively synthesized, and those compounds that were reported in > 2 studies and reportedly exhibited consistent associations with lung cancer were considered key breath biomarkers. A quality assessment was independently performed by two researchers using both the Newcastle-Ottawa Scale (NOS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).
resultsA total of 11 articles reporting on 46 VOC-based predictive models were included. The majority relied solely on exhaled VOCs (n = 44), while two incorporated VOCs, demographical factors, and radiological signs. The variation in the sensitivity, specificity, and AUC indicators of the models that incorporated multiple factors was lower compared with those of the models that relied solely on exhaled VOCs. A total of 84 VOCs were incorporated. Of these, 2-butanone, 3-hydroxy-2-butanone, and 2-hydroxyacetaldehyde were identified as key predictors that had significantly higher concentrations in the exhaled breath samples of patients with lung cancer. Substantial heterogeneity was observed in terms of the modeling and validation methods used, as well as the approaches to breath collection and analysis. Many of the reports were missing certain key pieces of clinical and methodological information.
conclusionsAlthough exhaled VOC-based models for predicting cancer risk might be a conceivable role as monitoring tools for PNs risk, there has been little overall change in the accuracy of these tests over time, and their role in routine clinical practice has not yet been established. CLINICAL TRIAL NUMBER: PROSPERO registration number CRD42023381458.
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