Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm
Yuichi Sakumura,
Yutaro Koyama,
Hiroaki Tokutake,
Toyoaki Hida,
Kazuo Sato,
Toshio Itoh,
Takafumi Akamatsu,
Woosuck Shin
Affiliations
Yuichi Sakumura
Department of Information Science and Technology, Aichi Prefectural University, Nagakute 480-1198, Japan
Yutaro Koyama
Department of Information Science and Technology, Aichi Prefectural University, Nagakute 480-1198, Japan
Hiroaki Tokutake
Department of Information Science and Technology, Aichi Prefectural University, Nagakute 480-1198, Japan
Toyoaki Hida
Department of Thoracic Oncology, Aichi Cancer Center, 1-1 Kanokoden, Chikusa-ku, Nagoya 464-8681, Japan
Kazuo Sato
Department of Mechanical Engineering, Aichi Institute of Technology, Toyota, 470-0392, Japan
Toshio Itoh
Department of Materials and Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), Shimo-Shidami, Moriyama-ku, Nagoya 463-8560, Japan
Takafumi Akamatsu
Department of Materials and Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), Shimo-Shidami, Moriyama-ku, Nagoya 463-8560, Japan
Woosuck Shin
Department of Materials and Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), Shimo-Shidami, Moriyama-ku, Nagoya 463-8560, Japan
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.
