Early non-invasive detection of breast cancer using exhaled breath and urine analysis

Or Herman-Saffar, Zvi Boger, Shai Libson, David Lieberman, Raphael Gonen, Yehuda Zeiri

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


The main focus of this pilot study is to develop a statistical approach that is suitable to model data obtained by different detection methods. The methods used in this study examine the possibility to detect early breast cancer (BC) by exhaled breath and urine samples analysis. Exhaled breath samples were collected from 48 breast cancer patients and 45 healthy women that served as a control group. Urine samples were collected from 37 patients who were diagnosed with breast cancer based on physical or mammography tests prior to any surgery, and from 36 healthy women. Two commercial electronic noses (ENs) were used for the exhaled breath analysis. Urine samples were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS). Statistical analysis of results is based on an artificial neural network (ANN) obtained following feature extraction and feature selection processes. The model obtained allows classification of breast cancer patients with an accuracy of 95.2% ± 7.7% using data of one EN, and an accuracy of 85% for the other EN and for urine samples. The developed statistical analysis method enables accurate classification of patients as healthy or with BC based on simple non-invasive exhaled breath and a urine sample analysis. This study demonstrates that available commercial ENs can be used, provided that the data analysis is carried out using an appropriate scheme.

Original languageEnglish
Pages (from-to)227-232
Number of pages6
JournalComputers in Biology and Medicine
StatePublished - 1 May 2018


  • Artificial neural networks
  • Breast cancer diagnosis
  • Exhaled breath
  • Urine

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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