无标记共焦显微拉曼光谱区分脑肿瘤。

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy.

机构信息

Chongqing Medical University, Chongqing, 400016, China; Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Academy of Sciences, Chongqing, 400714, China.

Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.

出版信息

Photodiagnosis Photodyn Ther. 2024 Feb;45:104010. doi: 10.1016/j.pdpdt.2024.104010. Epub 2024 Feb 7.

DOI:10.1016/j.pdpdt.2024.104010

PMID:38336147

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 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积极的治疗至关重要，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）用于降低拉曼光谱数据的维度。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

从 40 个脑肿瘤中采集拉曼光谱。不同脑肿瘤组织的拉曼光谱在 721、854、1004、1032、1128、1248、1449cm 处的强度和位移发生变化。PCA 结果表明，胶质瘤、垂体腺瘤和脑膜瘤彼此之间难以区分，而听神经瘤与其他三种肿瘤明显不同。包括 QDA 和 LDA 方法在内的多元分析表明，QDA 模型的分类准确率为 99.47%，优于 LDA 模型的 95.07%。