This work aimed to establish a fast and accurate method to detect glioma by combining surface-enhanced Raman scattering (SERS) and mathematical analysis. At first, 785-nm laser was selected as the optimum laser to acquire Raman spectra of human brain tissue. Second, it was verified that Raman data in the range of 1,200-1,600 cm(-1) could improve the performance of classifier. Based on the analytical results of 1,200-1,600 cm(-1) data, the sensitivity and specificity of partial least square (PLS) analysis and back-propagation neural network (BPNN) were as high as 100%, whereas the sensitivity and specificity of support vector machine (SVM) were 96% and 100%, respectively. Among them, PLS was more potential in the detection of glioma, because of its lower computational cost compared with SVM and BPNN. Moreover, the correlation between observed Raman peaks and 2-hydroxyglutarate (2HG; 512, 790, 1,204, 1,302, and 1,463 cm(-1)) was observed, suggesting 2HG as a potential marker of glioma using its Raman spectroscopic signatures. After all, SERS combining with mathematical analysis could be a promising tool for the accurate detection of glioma.
[1]Univ Sci & Technol China, Coll Biomed Engn, Hefei, Anhui, Peoples R China;
[2]Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, CAS Key Lab Biomed Diagnost, 88 Keling Rd, Suzhou 215163, Jiangsu, Peoples R China;
[3]Fudan Univ, Huashan Hosp, Dept Neurosurg, 12 Wulumuqi Zhong Rd, Shanghai 200040, Peoples R China
(8.0+极速模式)