Research Papers: Sensing

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

[+] Author Affiliations
Shao-Xin Li

South China Normal University, School of Information and Optoelectronic Science and Engineering, Guangzhou 510631, China

Guangdong Medical College, School of Information Engineering, Dongguan 523808, China

Qiu-Yao Zeng, Lin-Fang Li

Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Department of Clinical Laboratory, Guangzhou, 510060, China

Yan-Jiao Zhang

Guangdong Medical College, School of Basic Medicine, Dongguan 523808, China

Ming-Ming Wan, Zhi-Ming Liu, Hong-Lian Xiong, Zhou-Yi Guo, Song-Hao Liu

South China Normal University, School of Information and Optoelectronic Science and Engineering, Guangzhou 510631, China

J. Biomed. Opt. 18(2), 027008 (Feb 06, 2013). doi:10.1117/1.JBO.18.2.027008
History: Received November 8, 2012; Revised December 29, 2012; Accepted January 17, 2013
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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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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Shao-Xin Li ; Qiu-Yao Zeng ; Lin-Fang Li ; Yan-Jiao Zhang ; Ming-Ming Wan, et al.
"Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection", J. Biomed. Opt. 18(2), 027008 (Feb 06, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.2.027008


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