Teh S K, Zheng W, Ho K Y, Teh M, Yeoh K G, Huang Z
Bioimaging Laboratory, Division of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore 117576, Singapore.
Br J Cancer. 2008 Jan 29;98(2):457-65. doi: 10.1038/sj.bjc.6604176. Epub 2008 Jan 15.
Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out cross-validation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200-1500 cm(-1) and 1600-1800 cm(-1), which contained signals related to amide III and amide I of proteins, CH(3)CH(2) twisting of proteins/nucleic acids, and the C=C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm(-1) to the peak intensity at 1450 cm(-1) gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules.
拉曼光谱是一种分子振动光谱技术,能够通过光学手段探测与疾病转变相关的生物分子变化。本研究的目的是探索近红外(NIR)拉曼光谱用于从正常胃黏膜组织中识别发育异常。采用快速采集色散型近红外拉曼系统,在785nm激光激发下进行组织拉曼光谱测量。本研究共使用了从44例接受内镜检查或胃切除术的患者中获取的76份胃组织样本。组织病理学检查显示,55份组织标本正常,21份为发育异常。经验方法和多变量统计技术,包括主成分分析(PCA)和线性判别分析(LDA),以及留一法交叉验证方法,均被用于开发有效的诊断算法,以对正常和发育异常胃组织的拉曼光谱进行分类。在5秒内即可从胃组织中获取800 - 1800 cm(-1)范围内的高质量拉曼光谱。正常组织和发育异常组织的拉曼光谱存在特定的光谱差异,特别是在1200 - 1500 cm(-1)和1600 - 1800 cm(-1)光谱范围内,分别包含与蛋白质的酰胺III和酰胺I、蛋白质/核酸的CH(3)CH(2)扭曲以及磷脂的C = C伸缩模式相关的信号。基于875 cm(-1)处拉曼峰强度与1450 cm(-1)处峰强度之比的经验诊断算法,诊断灵敏度为85.7%,特异性为80.0%,而基于PCA - LDA的诊断算法在区分发育异常胃组织与正常胃组织时,诊断灵敏度为95.2%,特异性为90.9%。受试者工作特征(ROC)曲线进一步证实,最有效的诊断算法可源自PCA - LDA技术。因此,近红外拉曼光谱结合多变量统计技术,基于生物分子光谱特征的光学评估,具有快速诊断胃发育异常的潜力。