Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering
Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2023-03-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545822440011 |
| _version_ | 1797865853721509888 |
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| author | Xiao Ma Honglian Xiong Jinhao Guo Zhiming Liu Yaru Han Mingdi Liu Yanxian Guo Mingyi Wang Huiqing Zhong Zhouyi Guo |
| author_facet | Xiao Ma Honglian Xiong Jinhao Guo Zhiming Liu Yaru Han Mingdi Liu Yanxian Guo Mingyi Wang Huiqing Zhong Zhouyi Guo |
| author_sort | Xiao Ma |
| collection | DOAJ |
| description | Because the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm[Formula: see text]). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future. |
| first_indexed | 2024-04-09T23:15:51Z |
| format | Article |
| id | doaj.art-e9a6284480704fcc95b706a218110b48 |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-04-09T23:15:51Z |
| publishDate | 2023-03-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-e9a6284480704fcc95b706a218110b482023-03-22T10:03:09ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052023-03-01160210.1142/S1793545822440011Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scatteringXiao Ma0Honglian Xiong1Jinhao Guo2Zhiming Liu3Yaru Han4Mingdi Liu5Yanxian Guo6Mingyi Wang7Huiqing Zhong8Zhouyi Guo9MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaDepartment of Physics and Optoelectronic Engineering, Foshan University, Guangdong 528011, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaDepartment of Physics and Optoelectronic Engineering, Foshan University, Guangdong 528011, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaDepartment of Physics and Optoelectronic Engineering, Foshan University, Guangdong 528011, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, SATCM Third Grade Laboratory of Chinese Medicine and Photonics Technology & Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, P. R. ChinaBecause the breast cancer is an important factor that threatens women’s lives and health, early diagnosis is helpful for disease screening and a good prognosis. Exosomes are nanovesicles, secreted from cells and other body fluids, which can reflect the genetic and phenotypic status of parental cells. Compared with other methods for early diagnosis of cancer (such as circulating tumor cells (CTCs) and circulating tumor DNA), exosomes have a richer number and stronger biological stability, and have great potential in early diagnosis. Thus, it has been proposed as promising biomarkers for diagnosis of early-stage cancer. However, distinguishing different exosomes remain is a major biomedical challenge. In this paper, we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering (SERS). As a result, it can be seen from the SERS spectra that the exosomes of MCF-7, MDA-MB-231 and MCF-10A cells have similar peaks (939, 1145 and 1380 cm[Formula: see text]). Based on this dataset, the predictive model can achieve 95% accuracy. Compared with principal component analysis (PCA), the trained CNN can classify exosomes from different breast cancer cells with a superior performance. The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells, SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.https://www.worldscientific.com/doi/10.1142/S1793545822440011Exosomessurface-enhanced Raman scattering (SERS)breast cancerconvolutional neural modellabel-free |
| spellingShingle | Xiao Ma Honglian Xiong Jinhao Guo Zhiming Liu Yaru Han Mingdi Liu Yanxian Guo Mingyi Wang Huiqing Zhong Zhouyi Guo Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering Journal of Innovative Optical Health Sciences Exosomes surface-enhanced Raman scattering (SERS) breast cancer convolutional neural model label-free |
| title | Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering |
| title_full | Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering |
| title_fullStr | Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering |
| title_full_unstemmed | Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering |
| title_short | Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering |
| title_sort | label free breast cancer detection and classification by convolutional neural network based on exosomes surface enhanced raman scattering |
| topic | Exosomes surface-enhanced Raman scattering (SERS) breast cancer convolutional neural model label-free |
| url | https://www.worldscientific.com/doi/10.1142/S1793545822440011 |
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