摘要
目的:研究正常胃粘膜和胃癌组织在拉曼光谱指纹区(800-1 800 cm-1)和高波数区(2 800-3 000 cm-1)的光谱特征,并将其联合使用建立胃癌诊断模型。方法:收集38例正常胃粘膜和37例胃癌组织活检标本,采用785 nm激发光拉曼光谱仪进行拉曼光谱采集。比较正常胃粘膜和胃癌组织在指纹区和高波数区的拉曼光谱异同,使用偏最小二乘判别分析(PLS-DA)结合留一法交叉验证建立诊断模型。结果:1)胃癌组织在853 cm^(-1),879cm^(-1),1 003 cm^(-1),1 047 cm^(-1),1 173 cm-1,1 304 cm^(-1),1 319 cm^(-1),1 338 cm^(-1),1 374 cm-1、2 932 cm-1谱峰处与正常胃粘膜的拉曼峰强度差异有统计学意义(P<0.05)2)将拉曼光谱指纹区和高波数区联合,利用PLS-DA建立胃癌诊断模型的敏感性为94.59%(35/37),特异性为86.84(33/38),正确率为90.6%(68/75)。结论:正常胃粘膜和胃癌组织在拉曼光谱指纹区和高波数区均有显著差异,将上述两区联合使用建立模型诊断胃癌能取得良好的诊断效果。
Object: The aim of this study was to apply Raman spectroscopy in the fingerprint( FP) region( 800-1 800cm-1) and the High-wavenumber( HW) region( 2 800-3 000 cm-1) for ex vivo detection of gastric cancer and compare the difference of the Raman spectra between the normal mucosa and the gastric cancer. Method: Raman spectra were collected to differentiate normal mucosa( n = 38) and gastric cancer( n = 37) in the FP region and the HW region by Micro-Raman Spectroscopy System at 785-nm excitation. Multivariate diagnostic algorithms was generated based on partial least-squares-discriminant analysis( PLS-DA) together with the leave-one-sample-out,cross-validation. Results:1) The significant Raman spectral difference between normal and cancer tissue was observed at 853 cm^(-1),879 cm^(-1),1 003cm-1,1 047 cm-1,1 173 cm-1,1 304 cm-1,1 319 cm^(-1),1 338 cm^(-1),1 374 cm^(-1)、2 932 cm^(-1)( P < 0. 05). Multivariate diagnostic algorithms yield the diagnostic sensitivities of 94. 59%( 35 /37),specificities of 86. 84%( 33 /38),diagnostic accuracies of 90. 6%( 68 /75),using the integrated FP / HW Raman spectroscopy for ex vivo diagnosis of gastric cancer.Conclusion: There are distinct differences between the normal mucosa and gastric cancer in the FP region and the HW region. Multivariate diagnostic algorithms using the integrated FP / HW Raman spectroscopy performs well in the diagnoses of gastric cancer.
出处
《激光生物学报》
CAS
2016年第1期20-25,共6页
Acta Laser Biology Sinica
基金
国家自然科学基金项目(81272580)
关键词
胃癌
拉曼光谱
指纹区
高波数区
偏最小二乘判别分析
gastric cancer
Raman spectroscopy
high-wavenumber(HW)
fingerprint(FP)
partial least-squares discriminant analysis(PLS-DA)