Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

• Sakumura, Yuichi

• Koyama, Yutaro

• Tokutake, Hiroaki

• Hida, Toyoaki

• Itoh, Toshio

• Akamatsu, Takafumi

• Shin, Woosuck

抄録

Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH3CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer.KW - lung cancer

収録刊行物

• Sensors

  Sensors 17 (2), 287-, 2017-02-04

  MDPI

キーワード

• lung cancer
• volatile organic compounds (VOCs)
• exhaled air
• screening
• gas chromatography?mass spectrometry analysis
• support vector machine (SVM)

詳細情報

• CRID: 1050577309352774912
• NII論文ID: 120006537399
• ISSN: 14248220
• DOI: 10.3390/s17020287
• HANDLE: 10061/12595
• Web Site: https://naist.repo.nii.ac.jp/records/9205
• Web Site: http://www.mdpi.com/1424-8220/17/2/287/pdf
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
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  • Crossref
  • CiNii Articles
  • KAKEN