Trial reportScientific reports2021
Breath biopsy of breast cancer using sensor array signals and machine learning analysis.
Trial report in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 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
- Pooled it
- Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.Biosensors · 2021Pooled it
- Advanced breath analysis through hierarchical deep convolutional neural network for multi-cancer screening.NPJ digital medicine · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Systematic comparison of methods for offline breath sampling.Analytical and bioanalytical chemistry · 2025Article
- Review
- Breath fingerprint of colorectal cancer patients by gas chromatography-mass spectrometry analysis preparatory to e-nose analyses.Practical laboratory medicine · 2025Article
- Innovative Methodologies for the Early Detection of Breast Cancer: A Review Categorized by Target Biological Samples.Biosensors · 2025Review
- Breathomics: A Non-Invasive Approach for the Diagnosis of Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Review
- Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath.Sensors (Basel, Switzerland) · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer.Biosensors · 2025Article
- Utility of Volatile Organic Compounds and Electronic Nose Technology for Breast Cancer Detection: A Systematic Review.Breast cancer (Dove Medical Press) · 2025Review
- Artificial Intelligence-Powered Nanosensor Platforms for Non-Invasive Breathomic Diagnostics.Nanotechnology, science and applications · 2025Review
- The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in the exhaled breath. We collected alveolar air from breast cancer patients and non-cancer controls and analyzed the volatile metabolites with an electronic nose composed of 32 carbon nanotubes sensors. We used machine learning techniques to build prediction models for breast cancer and its molecular phenotyping. Between July 2016 and June 2018, we enrolled a total of 899 subjects. Using the random forest model, the prediction accuracy of breast cancer in the test set was 91% (95% CI: 0.85-0.95), sensitivity was 86%, specificity was 97%, positive predictive value was 97%, negative predictive value was 97%, the area under the receiver operating curve was 0.99 (95% CI: 0.99-1.00), and the kappa value was 0.83. The leave-one-out cross-validated discrimination accuracy and reliability of molecular phenotyping of breast cancer were 88.5 ± 12.1% and 0.77 ± 0.23, respectively. Breath tests with electronic noses can be applied intraoperatively to discriminate breast cancer and molecular subtype and support the medical staff to choose the best therapeutic decision.
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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.