ArticleMolecules (Basel, Switzerland)2021
Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Differentiating superficial fungal infection from eczema using a heated dynamic-headspace skin VOC sampler: a hypothesis.Frontiers in medicine · 2026Article
- Determination of lung cancer exhaled breath biomarkers using machine learning-a new analysis framework.Scientific reports · 2025Article
- Collection methods of exhaled volatile organic compounds for lung cancer screening and diagnosis: a systematic review.Journal of thoracic disease · 2024Review
- Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer.Scientific reports · 2023Article
- Review
- GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review.Frontiers in molecular biosciences · 2023Review
- Experiences and Perspectives of GC-MS Application for the Search of Low Molecular Weight Discriminants of Schizophrenia.Molecules (Basel, Switzerland) · 2022Review
- Analysis of endogenous metabolites using multifunctional derivatization and capillary RPLC-MS.Analytical methods : advancing methods and applications · 2022Article
- Analyses of lung cancer-derived volatiles in exhaled breath andExperimental biology and medicine (Maywood, N.J.) · 2022Review
- pyAIR-A New Software Tool for Breathomics Applications-Searching for Markers in TD-GC-HRMS Analysis.Molecules (Basel, Switzerland) · 2022Article
- Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors.Sensors (Basel, Switzerland) · 2022Review
- Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer.Metabolites · 2022Article
- A Panel of Bile Volatile Organic Compounds Servers as a Potential Diagnostic Biomarker for Gallbladder Cancer.Frontiers in oncology · 2022Article
- Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities.Biosensors · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
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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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.