Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure

Xiaorong Ma; Hong Cheng; Junwei Hou; Zhenhong Jia; Guohua Wu; Xiaoyi Lü; Hongyi Li; Xiangxiang Zheng; Chen Chen

doi:doi:10.3788/COL202018.051701

Chinese Optics Letters, 2020, 18 (5): 051701, Published Online: May. 6, 2020

Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure Download： 879次

Xiaorong Ma ¹Hong Cheng ²Junwei Hou ³Zhenhong Jia ^4,*Guohua Wu ⁵Xiaoyi Lü ^4,**Hongyi Li ⁶Xiangxiang Zheng ⁵Chen Chen ⁴

Author Affiliations

¹ School of Physical Science and Technology, Xinjiang University, Urumqi 830046, China

² Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830000, China

³ Department of Engineering, China University of Petroleum-Beijing, Keramayi 834000, China

⁴ School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China

⁵ School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

⁶ Quality of Products Supervision and Inspection Institute, Urumqi 830011, China

Figures & Tables

Fig. 1. SEM images of the newly prepared SERS substrates: (a) surface morphology; (b) cross-sectional morphology; (c) partial enlarged view of (b); (d) surface morphology of the composite Ag NPs PSi Bragg reflector SERS substrate.

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Fig. 2. SERS spectra of early BRC serum samples from 30 patients on monocrystalline silicon, composite Ag NPs single-layer PSi substrates, and the composite Ag NPs PSi Bragg reflector SERS substrate.

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Fig. 3. SERS spectra of early BRC serum samples (diluted in different proportions) on the composite Ag NPs PSi Bragg reflector SERS substrate.

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Fig. 4. (a) Plot of PC1 versus PC2 for the normal group versus the BRC group. (b) Plot of PC1 versus PC3 for the normal group versus the BRC group. (c) Plot of PC2 versus PC3 for the normal group versus the BRC group. (d) A three-dimensional mapping of the PCA results for the normal group and BRC group with PC1, PC2, and PC3 as the three axes. (The black triangle represents the normal group; the red triangle represents the BRC group.)

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Fig. 5. Cumulative variance contribution rate and LDA classification accuracy as a function of the number of principal components.

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Table1. Confusion Matrix for the LDA Algorithm^[22]

Confusion matrix		Histopathological reference class
Confusion matrix		Normal	Cancer
Predicted class	Normal	28	2
Predicted class	Cancer	1	29
Cross validation (%)	Normal	93.3	6.7
Cross validation (%)	Cancer	3.3	96.7

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Xiaorong Ma, Hong Cheng, Junwei Hou, Zhenhong Jia, Guohua Wu, Xiaoyi Lü, Hongyi Li, Xiangxiang Zheng, Chen Chen. Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure[J]. Chinese Optics Letters, 2020, 18(5): 051701.

Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure Download： 879次

Fig. 1. SEM images of the newly prepared SERS substrates: (a) surface morphology; (b) cross-sectional morphology; (c) partial enlarged view of (b); (d) surface morphology of the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 2. SERS spectra of early BRC serum samples from 30 patients on monocrystalline silicon, composite Ag NPs single-layer PSi substrates, and the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 3. SERS spectra of early BRC serum samples (diluted in different proportions) on the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 5. Cumulative variance contribution rate and LDA classification accuracy as a function of the number of principal components.

Table1. Confusion Matrix for the LDA Algorithm^[22]

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Detection of breast cancer based on novel porous silicon Bragg reflector surface-enhanced Raman spectroscopy-active structure Download： 879次

Fig. 1. SEM images of the newly prepared SERS substrates: (a) surface morphology; (b) cross-sectional morphology; (c) partial enlarged view of (b); (d) surface morphology of the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 2. SERS spectra of early BRC serum samples from 30 patients on monocrystalline silicon, composite Ag NPs single-layer PSi substrates, and the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 3. SERS spectra of early BRC serum samples (diluted in different proportions) on the composite Ag NPs PSi Bragg reflector SERS substrate.

Fig. 5. Cumulative variance contribution rate and LDA classification accuracy as a function of the number of principal components.

Table1. Confusion Matrix for the LDA Algorithm[22]

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Table1. Confusion Matrix for the LDA Algorithm^[22]