Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

Shao-Xin Li; Qiu-Yao Zeng; Lin-Fang Li; Yan-Jiao Zhang; Ming-Ming Wan; Zhi-Ming Liu; Hong-Lian Xiong; Zhou-Yi Guo; Song-Hao Liu

doi:10.1117/1.JBO.18.2.027008

6 February 2013 Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

Shao-Xin Li, Qiu-Yao Zeng, Lin-Fang Li, Yan-Jiao Zhang, Ming-Ming Wan, Zhi-Ming Liu, Hong-Lian Xiong, Zhou-Yi Guo, Song-Hao Liu

Author Affiliations +

Funded by: Dongguan Science and Technology, National Natural Science Foundation of China, Natural Science Foundation of China, Doctoral Program of Higher Education of China, Natural Science Foundation of Guangdong Province, Natural Science Foundation of Guangdong Province of China, Ministry of Education of China, Cooperation Project in Industry, Education and Research of Guangdong province and Ministry of Education of China

Journal of Biomedical Optics, Vol. 18, Issue 2, 027008 (February 2013). https://doi.org/10.1117/1.JBO.18.2.027008

Abstract

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30 ) and the other group from healthy volunteers (n=31 ). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

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Shao-Xin Li, Qiu-Yao Zeng, Lin-Fang Li, Yan-Jiao Zhang, Ming-Ming Wan, Zhi-Ming Liu, Hong-Lian Xiong, Zhou-Yi Guo, and Song-Hao Liu "Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection," Journal of Biomedical Optics 18(2), 027008 (6 February 2013). https://doi.org/10.1117/1.JBO.18.2.027008

Published: 6 February 2013

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