Li Bo, Gu Zhi-Yu, Yan Kai-Xiao, Wen Zhi-Ning, Zhao Zhi-He, Li Long-Jiang, Li Yi
State Key Laboratory of Oral Disease, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
Oncotarget. 2017 Jul 18;8(44):76257-76265. doi: 10.18632/oncotarget.19343. eCollection 2017 Sep 29.
Until now, the classification system of oral epithelial dysplasia is still based on the architectural and cytological changes, which relies on the observation of pathologists and is relatively subjective. The purpose of present research was to discriminate the oral dysplasia by the near-infrared Raman spectroscope, in order to evaluate the classification system. We collected Raman spectra of normal mucosa, oral squamous cell carcinoma (OSCC) and dysplasia by near-infrared Raman spectroscope. The biochemical variations between different stages were analyzed by the characteristic peaks in the subtracted mean spectra. Gaussian radial basis function support vector machines (SVM) were used to establish the diagnostic models. At the same time, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to verify the results of SVM. Raman spectral differences were observed in the range between 730~1913 cm. Compared with normal mucosa, high contents of protein and DNA in oral dysplasia and OSCC were observed. There were no significant or gradual variation of Raman peaks among different dysplastic grades. The accuracies of comparison between mild, moderate, severe dysplasia with OSCC were 100%, 44.44%, 71.15%, which elucidated the low modeling ability of support vector machines, especially for the moderate dysplasia. The analysis by PCA-LDA could not discriminate the stages, either. Combined with support vector machines, near-infrared Raman spectroscopy could detect the biochemical variations in oral normal, OSCC and dysplastic tissues, but could not establish diagnostic model accurately. The classification system needs further improvements.
到目前为止，口腔上皮发育异常的分类系统仍基于结构和细胞学变化，这依赖于病理学家的观察，且相对主观。本研究的目的是通过近红外拉曼光谱仪鉴别口腔发育异常，以评估该分类系统。我们用近红外拉曼光谱仪收集了正常黏膜、口腔鳞状细胞癌（OSCC）和发育异常组织的拉曼光谱。通过减去平均光谱中的特征峰分析不同阶段之间的生化变化。使用高斯径向基函数支持向量机（SVM）建立诊断模型。同时，使用主成分分析（PCA）和线性判别分析（LDA）验证SVM的结果。在730~1913 cm范围内观察到拉曼光谱差异。与正常黏膜相比，口腔发育异常和OSCC中蛋白质和DNA含量较高。不同发育异常等级之间的拉曼峰没有显著或逐渐变化。轻度、中度、重度发育异常与OSCC之间比较的准确率分别为100%、44.44%、71.15%，这说明支持向量机的建模能力较低，尤其是对中度发育异常。PCA-LDA分析也无法区分各阶段。结合支持向量机，近红外拉曼光谱可以检测口腔正常组织、OSCC和发育异常组织中的生化变化，但不能准确建立诊断模型。该分类系统需要进一步改进。
