ArticleMetabolites2023
Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities.
Article in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Advanced strategy for cancer detection based on volatile organic compounds in breath.Journal of nanobiotechnology · 2025Pooled it
- Pooled it
- Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study.Scientific reports · 2024Trial
- Harnessing breath biomarkers for pneumonia diagnosis and prognosis.Expert review of respiratory medicine · 2026Review
- Urinary Volatilomic Signatures for Non-Invasive Detection of Lung Cancer: A HS-SPME/GC-MS Proof-of-Concept Study.International journal of molecular sciences · 2026Article
- Exploratory analysis of exhaled volatile organic compounds for binary discrimination between lung cancer, pneumonia, and healthy controls using machine learning.Frontiers in medicine · 2026Article
- Exploring exhaled volatile organic compounds as potential biomarkers in anti-MDA5 antibody-positive interstitial lung disease.Molecular and cellular biochemistry · 2025Article
- Exploring Exhaled Breath Analysis in Adults With Chronic Visceral Acid Sphingomyelinase Deficiency to Identify Potential Biomarkers of Pulmonary Involvement.Journal of inherited metabolic disease · 2025Article
- Volatile Organic Compounds in Biological Matrices as a Sensitive Weapon in Cancer Diagnosis.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Collection methods of exhaled volatile organic compounds for lung cancer screening and diagnosis: a systematic review.Journal of thoracic disease · 2024Review
- Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.Respiratory research · 2024Article
- A matrix-centered view of mass spectrometry platform innovation for volatilome research.Frontiers in molecular biosciences · 2024Review
- Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.
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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.