Wang Yimeng, Huang Da, Shu Kaiqiang, Xu Yingtong, Duan Yixiang, Fan Qingwen, Lin Qingyu, Tuchin Valery V
Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China.
Institute of Physics and Science Medical Center, Saratov State University, Saratov, Russia.
J Biophotonics. 2023 Nov;16(11):e202300239. doi: 10.1002/jbio.202300239. Epub 2023 Aug 9.
The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.
癌症的快速准确诊断是临床医学中的一个重要课题。在当前工作中，开发了一种基于激光诱导击穿光谱（LIBS）并结合机器学习的创新方法，用于区分和分类不同的肿瘤细胞系。首先获取细胞的LIBS光谱。然后进行光谱预处理以及详细优化，以提高分类准确率。之后，进一步比较卷积神经网络（CNN）、支持向量机（SVM）和K近邻算法对肿瘤细胞的优化分类能力。CNN算法和SVM算法在区分肿瘤细胞方面均取得了令人印象深刻的判别性能，准确率达到97.72%。结果表明，肿瘤细胞中元素的异质性在区分细胞方面起着重要作用。这也意味着LIBS技术可作为一种快速分类方法用于肿瘤细胞分类。
