Fast discrimination of tumor and blood cells by label-free surface-enhanced Raman scattering spectra and deep learning
Fast discrimination of tumor and blood cells by label-free surface-enhanced Raman scattering spectra and deep learning
复制标题
基于无标记表面增强拉曼散射光谱和深度学习的肿瘤和血细胞快速识别
DOI：
10.1063/5.0042662
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发表时间：
2021-03-28
影响因子：
3.2
通讯作者：
Li, ShaoXin
中科院分区：
文献类型：
--
作者：
Fang, XiangLin;Zeng, QiuYao;Li, ShaoXin
快速准确地鉴定肿瘤细胞和血细胞是循环肿瘤细胞检测的重要组成部分。拉曼光谱是一种分子振动光谱技术，可以提供分子振动能级和转动能级的指纹信息。深度学习是一种先进的机器学习方法，可以用来对各种数据进行准确分类。本文用银膜衬底测量了血细胞和各种肿瘤细胞的表面增强拉曼散射光谱。发现大多数肿瘤细胞与血细胞在核酸相关特征峰上存在显著差异。这些光谱通过特征峰比法、结合k近邻的主成分分析法和残差网络进行分类，残差网络是一种深度学习算法。结果表明，比值法和主成分分析法结合k近邻法只能将部分肿瘤细胞与血细胞区分开来。残差网络可以快速识别各种肿瘤细胞和血细胞，准确率达到100%，并且不需要对表面增强拉曼散射光谱进行复杂的预处理。本研究表明，银膜表面增强拉曼散射技术结合深度学习算法可以快速准确地识别血细胞和肿瘤细胞，对无标记检测循环肿瘤细胞具有重要的参考价值。
Rapidly and accurately identifying tumor cells and blood cells is an important part of circulating tumor cell detection. Raman spectroscopy is a molecular vibrational spectroscopy technique that can provide fingerprint information about molecular vibrational and rotational energy levels. Deep learning is an advanced machine learning method that can be used to classify various data accurately. In this paper, the surface-enhanced Raman scattering spectra of blood cells and various tumor cells are measured with the silver film substrate. It is found that there are significant differences in nucleic acid-related characteristic peaks between most tumor cells and blood cells. These spectra are classified by the feature peak ratio method, principal component analysis combined with K-nearest neighbor, and residual network, which is a kind of deep learning algorithm. The results show that the ratio method and principal component analysis combined with the K-nearest neighbor method could only distinguish some tumor cells from blood cells. The residual network can quickly identify various tumor cells and blood cells with an accuracy of 100%, and there is no complex preprocessing for the surface-enhanced Raman scattering spectra. This study shows that the silver film surface-enhanced Raman scattering technology combined with deep learning algorithms can quickly and accurately identify blood cells and tumor cells, indicating an important reference value for the label-free detecting circulating tumor cells.