口腔鳞状细胞癌近红外拉曼光谱特征及其诊断价值研究

Near infrared Raman spectral character and diagnostic value of squamous cell carcinoma of oral mucosa

目的探索近红外拉曼光谱技术及化学计量法在口腔鳞状细胞癌诊断中的应用价值。方法收集正常口腔黏膜组织10例、鳞状细胞癌组织20例、白斑30例进行近红外拉曼光谱扫描,分析不同病变类型的特征性光谱,通过化学计量法进行分析建模,并评价其分类诊断效力。结果相对于正常组织,鳞状细胞癌及白斑中DNA、蛋白及脂类合成增强,表现出较明显的增殖活性。在对鳞状细胞癌和正常组织的建模比较中,诊断准确度98.81%;在鳞状细胞癌与白斑建模诊断中,诊断准确度96.30%。结论近红外拉曼光谱检测结合支持向量机分类建模技术,可以检测到口腔正常黏膜、白斑及鳞状细胞癌样本中的生化物质变化,并进行准确分类建模诊断。

Objective To evaluate the value of the near infrared Raman spectroscope in diagnosing oral squamous cell carcinoma（OSCC）.Methods Near infrared Raman spectra of ten normal mucosa,twenty OSCC and thirty oral leukoplakia（OLK） cases were collected in the research.Based on the previous researches,the information of the subtracted spectra of compared group was gained by the characteristic band in them.A Gaussian radial basis function support vector machine was used to classify spectra and establish the diagnostic models.The efficacy and validity of the algorithm were evaluated.Results By analyzing the subtracted mean spectra,the increasing peak intensity in wavenumber range of 500-2 200 cm^-1 hinted us of the high contents of DNA,protein and lipid in OSCC,which elucidate the high proliferative activity.The increasing peak intensity in the wavenumber range of 500-2 200 cm-1 hinted us of the high contents of DNA,protein and lipid in OSCC,which elucidate the high proliferative activity,but the difference between OLK and OSCC was not as much as that between normal and OSCC.The Gaussian radial basis function support vector machine showed powerful ability in grouping and modeling of normal and OSCC,and the specificity,sensitivity and accuracy were 100%,97.44% and 98.81% correspondingly.The algorithm showed good ability in grouping and modeling of OLK and OSCC,the specificity,sensitivity and accuracy were 95.00%,86.36% and 96.30%.Conclusion Combined with support vector machines,near infrared Raman spectroscopy could detect the biochemical variations in oral normal,OLK and OSCC,and establish diagnostic model accurately.