ArticleSensors (Basel, Switzerland)2017
Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.
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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
43 citing papers in PubMed, 4 syntheses or guidelines pooled it, 116 citations in OpenAlex.
- Advanced strategy for cancer detection based on volatile organic compounds in breath.Journal of nanobiotechnology · 2025Pooled it
- Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review.BMC pulmonary medicine · 2024Pooled it
- Pooled it
- Accuracy and Methodologic Challenges of Volatile Organic Compound-Based Exhaled Breath Tests for Cancer Diagnosis: A Systematic Review and Meta-analysis.JAMA oncology · 2019Pooled it
- Discriminant Profiles of Volatile Compounds in the Alveolar Air of Patients with Squamous Cell Lung Cancer, Lung Adenocarcinoma or Colon Cancer.Molecules (Basel, Switzerland) · 2021Trial
- Non-invasive detection of early-stage lung cancer through exhaled breath volatile organic compound analysis.Medical gas research · 2025Article
- Exhaled Breath Analysis: From Laboratory Test to Wearable Sensing.IEEE reviews in biomedical engineering · 2025Review
- Collection methods of exhaled volatile organic compounds for lung cancer screening and diagnosis: a systematic review.Journal of thoracic disease · 2024Review
- Global research and current trends on nanotherapy in lung cancer research: a bibliometric analysis of 20 years.Discover oncology · 2024Article
- Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.Sensors (Basel, Switzerland) · 2024Article
- Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.ACS sensors · 2024Article
- Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Fluorescent-based micellar incorporated hydrogel materials for selective determination of long-chain aldehydes.Mikrochimica acta · 2024Article
- MXene-Based Elastomer Mimetic Stretchable Sensors: Design, Properties, and Applications.Nano-micro letters · 2024Review
- Graphene as a Sensor for Lung Cancer: Insights into Adsorption of VOCs Using vdW DFT.ACS omega · 2024Article
- Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.International journal of medical sciences · 2024Review
- Biodetection of an odor signature in white-tailed deer associated with infection by chronic wasting disease prions.PloS one · 2024Article
- Bandgap engineering of germanene for gas sensing applications.RSC advances · 2023Article
- Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations.International journal of molecular sciences · 2023Article
- Metabolic biomarkers in lung cancer screening and early diagnosis (Review).Oncology letters · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH₃CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer.
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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.