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

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 le...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 17; H. 2; S. 287
Hauptverfasser: Sakumura, Yuichi, Koyama, Yutaro, Tokutake, Hiroaki, Hida, Toyoaki, Sato, Kazuo, Itoh, Toshio, Akamatsu, Takafumi, Shin, Woosuck
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 04.02.2017
MDPI
Schlagworte:
Algorithms
Breath Tests
Cancer
Diagnosis
Exhalation
exhaled air
Gas Chromatography-Mass Spectrometry
gas chromatography–mass spectrometry analysis
Humans
Lung cancer
Lung Neoplasms
Lungs
Patients
screening
Support Vector Machine
support vector machine (SVM)
Support vector machines
VOCs
Volatile compounds
Volatile Organic Compounds
volatile organic compounds (VOCs)
lung cancer
screening
exhaled air
gas chromatography–mass spectrometry analysis
volatile organic compounds (VOCs)
support vector machine (SVM)
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s17020287