Backscatter micro-spectra discrimination of liver cancer cell based on principal component analysis arithmetic and back propagation neural network_Journal of Biomedical Engineering

Authors：

YANG Jing ¹ ,  WANG Cheng ¹ , XIE Chengying ² , WENG Xiaofu ² , WEI Xunbin ²

1. Laboratory of Biomedical Optics & Optometry, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;
2. Institute of Med-X, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R.China;

Corresponding author：

Keywords：

micro-spectra; liver cancer cells; principal component analysis; back propagation neural network; cell classification

DOI：

10.7507/1001-5515.201605008

Video：

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In order to achieve the automatic identification of liver cancer cells in the blood, the present study adopted a principal component analysis (PCA) and back propagation (BP) algorithm of feedforward neural networks to identify white blood cells and red blood cells in mice and human liver cancer cells, HepG2. The present paper shows the process in which PCA was carried out after obtaining spectral data by fiber confocal back-scattering spectrograph, selecting the first two principal components as spectral features, and establishing a neural network pattern recognition model with two input layer nodes, eleven hidden layer nodes and three output nodes. In order to verify whether the model would give accurate identification of cells, we chose 195 object data to train the model with 150 sets of data as training set and 45 sets as test set. According to the results, the overall recognition accuracy of the three cells was above 90% with the average relative deviation only 4.36%. The results showed that PCA+BP algorithm could automatically identify liver cancer cells from erythrocyte and white blood cells, which will provide a useful tool for the study of metastasis and biological metabolism characteristics of liver cancer.

Citation： YANG Jing, WANG Cheng, XIE Chengying, WENG Xiaofu, WEI Xunbin. Backscatter micro-spectra discrimination of liver cancer cell based on principal component analysis arithmetic and back propagation neural network. Journal of Biomedical Engineering, 2017, 34(2): 246-252. doi: 10.7507/1001-5515.201605008 Copy

1.	沈玮博, 谭宏涛, 姜洪池. 肝细胞癌抗血管治疗: 现状及新视野. 中国医刊, 2015, 65(5): 25-27, 28.
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3.	马清, 陈水平, 陈建魁. 肝癌患者循环肿瘤细胞的检测及其临床应用研究进展. 标记免疫分析与临床, 2014, 21(5): 599-602, 617.
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5.	He Wei, Wang Haifeng, Hartmann L C, et al.In vivo quantitation of rare circulating tumor cells by multiphoton intravital flow cytometry. Proc Natl Acad Sci U S A, 2007, 104(28): 11760-11765.
6.	Galanzha E I, Shashkov E V, Kelly T, et al.In vivo magnetic enrichment and multiplex photoacoustic detection of circulating tumour cells. Nat Nanotechnol, 2009, 4(12): 855-860.
7.	Nedosekin D A, Sarimollaoglu M, Ye Jianhui, et al.In vivo ultra-fast photoacoustic flow cytometry of circulating human melanoma cells using near-infrared high-pulse rate lasers. Cytometry A, 2011, 79(10): 825-833.
8.	Biris A S, Galanzha E I, Li Zhongrui, et al. In vivo Raman flow cytometry for real-time detection of carbon nanotube kinetics in lymph, blood, and tissues. J Biomed Opt, 2009, 14(2): 021006.
9.	Greiner C, Hunter M, Huang P, et al. Confocal backscattering spectroscopy for leukemic and normal blood cell discrimination. Cytometry A, 2011, 79(10): 866-873.
10.	Lawrence B D, Cronin-Golomb M, Georgakoudi I A, et al. Bioactive silk protein biomaterial systems for optical devices. Biomacromolecules, 2008, 9(4): 1214-1220.
11.	王成, 方宝英, 蔡文杰, 等. 单细胞水平的光纤共焦后向散射显微光谱. 中国激光, 2009, 36(10): 2636-2641.
12.	倪晓宁, 董藩. 短期经济增长的综合影响因素研究及政策启示. 工业技术经济, 2006, 25(1): 71-73.
13.	王成, 白丽红, 文苗, 等. 基于 BP 神经网络的细胞显微光谱自动分类. 生命科学仪器, 2014, 12(6): 59-61.
14.	李志文, 蔡先发, 韦佳, 等. 基于 PCA 和 LDA 方法的肿瘤基因表达谱数据分类. 北京生物医学工程, 2014, 33(1): 47-51.
15.	刘炳胜, 王雪青, 林丽. 基于 PCA-BP 神经网络组合的中国建筑业竞争力预测. 北京理工大学学报:社会科学版, 2010, 12(6): 61-64.
16.	刘孝颂, 魏东东. 基于 BP 神经网络算法的 LTE 客户感知评价探讨. 互联网天地, 2015, 12(10): 28-33.
17.	陈先来, 肖晓旦, 杨荣, 等. 基于误差反向传播神经网络的胃癌细胞识别研究. 中国循证医学杂志, 2007, 7(9): 637-640.
18.	刘俊. BP 神经网络在多维非线性函数拟合中的应用. 商洛学院学报, 2014, 28(6): 19-22.

1. 沈玮博, 谭宏涛, 姜洪池. 肝细胞癌抗血管治疗: 现状及新视野. 中国医刊, 2015, 65(5): 25-27, 28.
2. 黄笛, 项楠, 唐文来, 等. 基于微流控技术的循环肿瘤细胞分选研究. 化学进展, 2015, 27(7): 882-912.
3. 马清, 陈水平, 陈建魁. 肝癌患者循环肿瘤细胞的检测及其临床应用研究进展. 标记免疫分析与临床, 2014, 21(5): 599-602, 617.
4. Li Yan, Guo Jin, Wang Chaofeng, et al. Circulation times of prostate cancer and hepatocellular carcinoma cells by in vivo flow cytometry. Cytometry A, 2011, 79(10): 848-854.
5. He Wei, Wang Haifeng, Hartmann L C, et al.In vivo quantitation of rare circulating tumor cells by multiphoton intravital flow cytometry. Proc Natl Acad Sci U S A, 2007, 104(28): 11760-11765.
6. Galanzha E I, Shashkov E V, Kelly T, et al.In vivo magnetic enrichment and multiplex photoacoustic detection of circulating tumour cells. Nat Nanotechnol, 2009, 4(12): 855-860.
7. Nedosekin D A, Sarimollaoglu M, Ye Jianhui, et al.In vivo ultra-fast photoacoustic flow cytometry of circulating human melanoma cells using near-infrared high-pulse rate lasers. Cytometry A, 2011, 79(10): 825-833.
8. Biris A S, Galanzha E I, Li Zhongrui, et al. In vivo Raman flow cytometry for real-time detection of carbon nanotube kinetics in lymph, blood, and tissues. J Biomed Opt, 2009, 14(2): 021006.
9. Greiner C, Hunter M, Huang P, et al. Confocal backscattering spectroscopy for leukemic and normal blood cell discrimination. Cytometry A, 2011, 79(10): 866-873.
10. Lawrence B D, Cronin-Golomb M, Georgakoudi I A, et al. Bioactive silk protein biomaterial systems for optical devices. Biomacromolecules, 2008, 9(4): 1214-1220.
11. 王成, 方宝英, 蔡文杰, 等. 单细胞水平的光纤共焦后向散射显微光谱. 中国激光, 2009, 36(10): 2636-2641.
12. 倪晓宁, 董藩. 短期经济增长的综合影响因素研究及政策启示. 工业技术经济, 2006, 25(1): 71-73.
13. 王成, 白丽红, 文苗, 等. 基于 BP 神经网络的细胞显微光谱自动分类. 生命科学仪器, 2014, 12(6): 59-61.
14. 李志文, 蔡先发, 韦佳, 等. 基于 PCA 和 LDA 方法的肿瘤基因表达谱数据分类. 北京生物医学工程, 2014, 33(1): 47-51.
15. 刘炳胜, 王雪青, 林丽. 基于 PCA-BP 神经网络组合的中国建筑业竞争力预测. 北京理工大学学报:社会科学版, 2010, 12(6): 61-64.
16. 刘孝颂, 魏东东. 基于 BP 神经网络算法的 LTE 客户感知评价探讨. 互联网天地, 2015, 12(10): 28-33.
17. 陈先来, 肖晓旦, 杨荣, 等. 基于误差反向传播神经网络的胃癌细胞识别研究. 中国循证医学杂志, 2007, 7(9): 637-640.
18. 刘俊. BP 神经网络在多维非线性函数拟合中的应用. 商洛学院学报, 2014, 28(6): 19-22.

Journal of Biomedical Engineering

Backscatter micro-spectra discrimination of liver cancer cell based on principal component analysis arithmetic and back propagation neural network

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