ArticleJournal of applied biomedicine2019
Volatile organic compounds of biofluids for detecting lung cancer by an electronic nose based on artificial neural network.
Article in Journal of applied biomedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 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
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- eNose technologies in the detection of cancer: a systematic review and meta-analysis.The oncologist · 2026Pooled it
- Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis.JAMA network open · 2022Pooled it
- Electronic nose versus gas chromatography - mass spectrometry for diagnostic discrimination of advanced hepatocellular carcinoma by volatolomic analysis.BMC gastroenterology · 2026Article
- Analysis of volatile organic compounds in biological samples of colorectal cancer patients using electronic nose-based machine learning techniques.Scientific reports · 2025Article
- Review
- Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics.Thoracic cancer · 2023Article
- Electronic nose versus VITEK 2 system for the rapid diagnosis of bloodstream infections.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Article
- The smell of lung disease: a review of the current status of electronic nose technology.Respiratory research · 2021Review
- Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.
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