ArticleCancers2020
Detection of Ovarian Cancer through Exhaled Breath by Electronic Nose: A Prospective Study.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 45 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
- Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis.JAMA network open · 2022Pooled it
- Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.Biosensors · 2021Pooled it
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.Cancers · 2025Review
- Volatile Organic Compounds in Biological Matrices as a Sensitive Weapon in Cancer Diagnosis.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Article
- Artificial Intelligence-Powered Nanosensor Platforms for Non-Invasive Breathomic Diagnostics.Nanotechnology, science and applications · 2025Review
- Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- A study of 9 common breath VOCs in 504 healthy subjects using PTR-TOF-MS.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Use of Sensor Array Analysis to Detect Ovarian Cancer through Breath, Urine, and Blood: A Case-Control Study.Diagnostics (Basel, Switzerland) · 2024Article
- Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends.Biomedicines · 2023Review
- Selectivity of Exhaled Breath Biomarkers of Lung Cancer in Relation to Cancer of Other Localizations.International journal of molecular sciences · 2023Article
- The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath.Sensors (Basel, Switzerland) · 2022Article
- Short-Term Effect of Cigarette Smoke on Exhaled Volatile Organic Compounds Profile Analyzed by an Electronic Nose.Biosensors · 2022Article
- Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors.Sensors (Basel, Switzerland) · 2022Review
- Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose.Bioresources and bioprocessing · 2021Article
- Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection.
methodsWomen with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose.
resultsE-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors' algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied.
conclusionsOur preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed.
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