Abstract：
BACKGROUND: Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.
METHODS: Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.
RESULTS: Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.
CONCLUSIONS: Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.
METHODS: Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.
RESULTS: Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.
CONCLUSIONS: Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.
摘要：
背景：脑肿瘤对公众健康和社会经济有严重的不良影响。准确检测脑肿瘤类型对于有效和主动治疗至关重要，从而提高患者的生存率。
方法：通过拉曼光谱检测四种类型的脑肿瘤组织切片。主成分分析（PCA）已用于降低拉曼光谱数据的维数。线性判别分析（LDA）和二次判别分析（QDA）方法用于区分不同类型的脑肿瘤。
结果：收集40个脑肿瘤的拉曼光谱。对于不同的脑肿瘤组织，在721、854、1004、1032、1128、1248、1449cm-1的拉曼光谱中观察到强度和位移的变化。PCA结果表明，胶质瘤，垂体腺瘤,和脑膜瘤很难区分，而听神经瘤与其他三种肿瘤有明显区别。包括QDA和LDA方法在内的多变量分析表明，QDA模型的分类准确率为99.47％，优于LDA模型的比率为95.07%。
结论：拉曼光谱可用于提取生物样品的指纹型分子和化学信息。演示的技术有可能被发展到一个快速的，无标签，和智能方法，以高精度区分脑肿瘤类型。
方法：通过拉曼光谱检测四种类型的脑肿瘤组织切片。主成分分析（PCA）已用于降低拉曼光谱数据的维数。线性判别分析（LDA）和二次判别分析（QDA）方法用于区分不同类型的脑肿瘤。
结果：收集40个脑肿瘤的拉曼光谱。对于不同的脑肿瘤组织，在721、854、1004、1032、1128、1248、1449cm-1的拉曼光谱中观察到强度和位移的变化。PCA结果表明，胶质瘤，垂体腺瘤,和脑膜瘤很难区分，而听神经瘤与其他三种肿瘤有明显区别。包括QDA和LDA方法在内的多变量分析表明，QDA模型的分类准确率为99.47％，优于LDA模型的比率为95.07%。
结论：拉曼光谱可用于提取生物样品的指纹型分子和化学信息。演示的技术有可能被发展到一个快速的，无标签，和智能方法，以高精度区分脑肿瘤类型。
