NDLI: Test of label-free Nasopharyngeal carinoma tissue at different stages by Raman spectroscopy

Content Provider	IEEE Xplore Digital Library
Author	Liu, Mingyu Qiu, Sufang Lin, Jinyong Wu, Weilin Chen, Guannan Chen, Rong
Copyright Year	2015
Description	Author affiliation: Key Laboratory of OptoElectonic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, 35007, China (Liu, Mingyu; Qiu, Sufang; Lin, Jinyong; Wu, Weilin; Chen, Guannan; Chen, Rong)
Abstract	Raman spectroscopy (RS) of Nasopharyngeal carcinoma (NPC) tissue contained various biomedicine features. These features indicated molecular-level information of tissue at different carcinoma development-level. This study suggested an automatic and quick method for the NPC Raman spectra classification at different stages by multivariate statistical analysis. In the RS measurement, high quality Raman spectra was acquired from each NPC tissue sample in two groups: one group consisted of 30 NPC patients at the early stages (I-II), another group was 46 NPC patients at the advanced stages (III-IV). Moreover, tentative diagnostic algorithms based on principle components analysis (PCA) and support vector machine (SVM) were employed to classify the multivariate data of Raman spectra effectively. The classification performance (sensitivities and specificities were 70% (21/30) and 91% (42/46)) was achieved by the PCA-SVM in conjunction with leave-one-out cross validation method. In this beneficial study, the RS technique in conjunction with PCA-SVM provided a great clinical potential for rapid NPC stage diagnosis.
Starting Page	224
Ending Page	228
File Size	302418
Page Count	5
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401505
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis support vector machine different stages Sensitivity and specificity principle components analysis Classification algorithms nasopharyngeal carcinoma tissue Raman spectroscopy Covariance matrices Principal component analysis
Content Type	Text
Resource Type	Article

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