| Title |
Prediction of breast cancer risk with volatile biomarkers in breath
|
|---|---|
| Published in |
Breast Cancer Research and Treatment, March 2018
|
| DOI | 10.1007/s10549-018-4764-4 |
| Pubmed ID | |
| Authors |
Michael Phillips, Renee N. Cataneo, Jose Alfonso Cruz-Ramos, Jan Huston, Omar Ornelas, Nadine Pappas, Sonali Pathak |
| Abstract |
Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Members of the public | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 102 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 17% |
| Student > Master | 17 | 17% |
| Researcher | 9 | 9% |
| Student > Bachelor | 9 | 9% |
| Student > Doctoral Student | 4 | 4% |
| Other | 16 | 16% |
| Unknown | 30 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 15% |
| Biochemistry, Genetics and Molecular Biology | 12 | 12% |
| Engineering | 6 | 6% |
| Agricultural and Biological Sciences | 6 | 6% |
| Medicine and Dentistry | 6 | 6% |
| Other | 20 | 20% |
| Unknown | 37 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 July 2018.
All research outputs
#14,979,439
of 23,043,346 outputs
Outputs from Breast Cancer Research and Treatment
#3,221
of 4,684 outputs
Outputs of similar age
#200,464
of 331,466 outputs
Outputs of similar age from Breast Cancer Research and Treatment
#39
of 68 outputs
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