| Title: | Breath biopsy of breast cancer using sensor array signals and machine learning analysis |
| Author(s): | Yang HY; Wang YC; Peng HY; Huang CH; |
| Address: | "Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei, Taiwan. Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan. Department of Anesthesiology, National Taiwan University College of Medicine, Taipei, Taiwan. Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan. Department of Anesthesiology, National Taiwan University College of Medicine, Taipei, Taiwan. tee.ntuh@gmail.com. Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan. tee.ntuh@gmail.com" |
| DOI: | 10.1038/s41598-020-80570-0 |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| 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" |
| Keywords: | "Adult Aged Biopsy/*methods Breast Neoplasms/diagnosis/*metabolism Breath Tests/*methods Case-Control Studies Electronic Nose Humans *Machine Learning Middle Aged Nanotubes, Carbon Volatile Organic Compounds/analysis/metabolism;" |
| Notes: | "MedlineYang, Hsiao-Yu Wang, Yi-Chia Peng, Hsin-Yi Huang, Chi-Hsiang eng Clinical Trial Research Support, Non-U.S. Gov't England 2021/01/10 Sci Rep. 2021 Jan 8; 11(1):103. doi: 10.1038/s41598-020-80570-0" |
