Breast Cancer Research and Treatment

, Volume 126, Issue 3, pp 791–796

Classification of breast cancer precursors through exhaled breath

  • Gregory Shuster
  • Zahava Gallimidi
  • Asnat Heyman Reiss
  • Ekaterina Dovgolevsky
  • Salem Billan
  • Roxolyana Abdah-Bortnyak
  • Abraham Kuten
  • Ahuva Engel
  • Ala Shiban
  • Ulrike Tisch
  • Hossam Haick
Brief Report

DOI: 10.1007/s10549-010-1317-x

Cite this article as:
Shuster, G., Gallimidi, Z., Reiss, A.H. et al. Breast Cancer Res Treat (2011) 126: 791. doi:10.1007/s10549-010-1317-x

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.

Keywords

Breast cancer Benign Classification Volatile biomarkers Breath Sensor NA-NOSE 

Abbreviations

IDC

Infiltrating ductal carcinoma

DCIS

Ductal carcinoma in situ

BC

Breast cancer

NA-NOSE

Nanoscale artificial NOSE

GC–MS

Gas-chromatography/mass-spectrometry

PCA

Principle component analysis

SVM

Support vector machine

Supplementary material

10549_2010_1317_MOESM1_ESM.pdf (137 kb)
Supplementary material 1 (PDF 137 kb)

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Gregory Shuster
    • 1
  • Zahava Gallimidi
    • 2
    • 3
  • Asnat Heyman Reiss
    • 2
  • Ekaterina Dovgolevsky
    • 1
  • Salem Billan
    • 4
  • Roxolyana Abdah-Bortnyak
    • 4
  • Abraham Kuten
    • 3
    • 4
  • Ahuva Engel
    • 2
    • 3
  • Ala Shiban
    • 1
  • Ulrike Tisch
    • 1
  • Hossam Haick
    • 1
  1. 1.The Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion—Israel Institute of TechnologyHaifaIsrael
  2. 2.Breast Imaging Unit, Radiology DepartmentThe Rambam Health Care CampusHaifaIsrael
  3. 3.Bruce Rappaport Faculty of MedicineTechnion—Israel Institute of TechnologyHaifaIsrael
  4. 4.Oncology DivisionRambam Health Care CampusHaifaIsrael

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