Breath biopsy of breast cancer using sensor array signals and machine learning analysis

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...

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Autores principales:	Hsiao-Yu Yang, Yi-Chia Wang, Hsin-Yi Peng, Chi-Hsiang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/745475a013f84d539d6724684ce65e83
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spelling	oai:doaj.org-article:745475a013f84d539d6724684ce65e832021-12-02T15:13:59ZBreath biopsy of breast cancer using sensor array signals and machine learning analysis10.1038/s41598-020-80570-02045-2322https://doaj.org/article/745475a013f84d539d6724684ce65e832021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80570-0https://doaj.org/toc/2045-2322Abstract 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.Hsiao-Yu YangYi-Chia WangHsin-Yi PengChi-Hsiang HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hsiao-Yu Yang Yi-Chia Wang Hsin-Yi Peng Chi-Hsiang Huang Breath biopsy of breast cancer using sensor array signals and machine learning analysis
description	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.
format	article
author	Hsiao-Yu Yang Yi-Chia Wang Hsin-Yi Peng Chi-Hsiang Huang
author_facet	Hsiao-Yu Yang Yi-Chia Wang Hsin-Yi Peng Chi-Hsiang Huang
author_sort	Hsiao-Yu Yang
title	Breath biopsy of breast cancer using sensor array signals and machine learning analysis
title_short	Breath biopsy of breast cancer using sensor array signals and machine learning analysis
title_full	Breath biopsy of breast cancer using sensor array signals and machine learning analysis
title_fullStr	Breath biopsy of breast cancer using sensor array signals and machine learning analysis
title_full_unstemmed	Breath biopsy of breast cancer using sensor array signals and machine learning analysis
title_sort	breath biopsy of breast cancer using sensor array signals and machine learning analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/745475a013f84d539d6724684ce65e83
work_keys_str_mv	AT hsiaoyuyang breathbiopsyofbreastcancerusingsensorarraysignalsandmachinelearninganalysis AT yichiawang breathbiopsyofbreastcancerusingsensorarraysignalsandmachinelearninganalysis AT hsinyipeng breathbiopsyofbreastcancerusingsensorarraysignalsandmachinelearninganalysis AT chihsianghuang breathbiopsyofbreastcancerusingsensorarraysignalsandmachinelearninganalysis
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Breath biopsy of breast cancer using sensor array signals and machine learning analysis

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