ArticleScientific reports2022
Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- 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
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.Cancers · 2025Review
- Online monitoring and stable isotope tracing of cancer associated volatiles in murine model captures tumor associated markers in vivo.Analytica chimica acta · 2025Article
- Volatile Organic Compounds in Biological Matrices as a Sensitive Weapon in Cancer Diagnosis.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Innovative Methodologies for the Early Detection of Breast Cancer: A Review Categorized by Target Biological Samples.Biosensors · 2025Review
- Non-Invasive Detection of Tumors by Volatile Organic Compounds in Urine.Biomedicines · 2025Review
- Utility of Volatile Organic Compounds and Electronic Nose Technology for Breast Cancer Detection: A Systematic Review.Breast cancer (Dove Medical Press) · 2025Review
- Metabolomics in cancer detection: A review of techniques, biomarkers, and clinical utility.BioMedicine · 2025Review
- MicroRNAs and their role in breast cancer metabolism (Review).International journal of oncology · 2025Review
- Evaluation of N-NOSE as a surveillance tool for recurrence in gastric and esophageal cancers: a prospective cohort study.BMC cancer · 2024Article
- Article
- The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Potential of Liver Serum Enzymes and SUVmax in Primary Tumors as Predictive Biomarkers With Correlational Evidence.Cureus · 2024Article
- Review
- Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis.International journal of molecular sciences · 2024Article
- Is the development of liquid biopsy for the early detection and the monitoring of breast cancers on its way of overtaking mammography?Frontiers in medicine · 2024Review
- Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models.American journal of cancer research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog's olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC-MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.
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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.