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Erschienen in: Breast Cancer Research and Treatment 3/2011

01.04.2011 | Brief Report

Classification of breast cancer precursors through exhaled breath

verfasst von: Gregory Shuster, Zahava Gallimidi, Asnat Heyman Reiss, Ekaterina Dovgolevsky, Salem Billan, Roxolyana Abdah-Bortnyak, Abraham Kuten, Ahuva Engel, Ala Shiban, Ulrike Tisch, Hossam Haick

Erschienen in: Breast Cancer Research and Treatment | Ausgabe 3/2011

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Abstract

Certain benign breast diseases are considered to be precursors of invasive breast cancer. Currently available techniques for diagnosing benign breast conditions lack accuracy. The purpose of this study was to deliver a proof-of-concept for a novel method that is based on breath testing to identify breast cancer precursors. Within this context, the authors explored the possibility of using exhaled alveolar breath to identify and distinguish between benign breast conditions, malignant lesions, and healthy states, using a small-scale, case-controlled, cross-sectional clinical trial. Breath samples were collected from 36 volunteers and were analyzed using a tailor-made nanoscale artificial NOSE (NA-NOSE). The NA-NOSE signals were analyzed using two independent methods: (i) principal component analysis, ANOVA and Student’s t-test and (ii) support vector machine analysis to detect statistically significant differences between the sub-populations. The NA-NOSE could distinguish between all studied test populations. Breath testing with a NA-NOSE holds future potential as a cost-effective, fast, and reliable diagnostic test for breast cancer risk factors and precursors, with possible future potential as screening method.
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Metadaten
Titel
Classification of breast cancer precursors through exhaled breath
verfasst von
Gregory Shuster
Zahava Gallimidi
Asnat Heyman Reiss
Ekaterina Dovgolevsky
Salem Billan
Roxolyana Abdah-Bortnyak
Abraham Kuten
Ahuva Engel
Ala Shiban
Ulrike Tisch
Hossam Haick
Publikationsdatum
01.04.2011
Verlag
Springer US
Erschienen in
Breast Cancer Research and Treatment / Ausgabe 3/2011
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-010-1317-x

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