Chinese Optics, Volume. 17, Issue 5, 995(2024)
Application of Raman spectroscopy in the detection of brain glioma
Fig. 1. The research state of the application of Raman spectroscopy in the detection of brain glioma
Fig. 2. (a) and (b) are photomicrographs of unstained human glioblastoma cryosections used in Raman mapping experiments; (c) and (d) are pseudo-color maps of Raman spectra of the tissue sections shown in (a) and (b). Red: areas of vital tumor tissue; blue: areas of necrosis; yellow: areas in the scan where no tissue was present (edges, freezing artifacts); (e) and (f) are photomicrographs of the same tissue sections after H&E staining; (g) ×40 magnification of detail marked with a green frame in (f)[34]
Fig. 3. (a) Raman spectra and (b) Raman imaging results of different components in invasive tissue[36]
Fig. 4. Comparison of Raman spectra of IDH1-wt and IDH1-mut glioma[39]
Fig. 5. The average Raman spectra of healthy (blue) and tumorous tissue (red)[39]
Fig. 7. (a) Mouse brain tissue with exposed cortex; (b) Raman images are segmented by cluster analysis. Normal brain tissue is depicted in blue, cyan, and yellow, the red is blood vessel, and the tumor and tumor margin are shown in gray and black, respectively; (c) superimposition of the photomicrograph and the Raman image of the tissue[42]
Fig. 8. (a) Schematic diagram of experimental setup of the handheld contact Raman spectroscopy probe for and (b) photo of brain tissue detection based on the probe[43]
Fig. 9. (a) Exploded view of the Raman microprobe components along the internal cannula of the commercial brain biopsy needle; (b) images from the neuronavigation system in the process of Raman detection[43]
Fig. 10. The Raman results of handheld contact probes at different tissue locations. (a) Schematic diagram of
Fig. 11. The identification results of normal brain, infiltrated brain and dense cancer tissue based on SVM algorithm. (a) Boxplots of the Raman intensity ratio of the lipid and protein in the bands of
Fig. 12. The ratios of (a)
Fig. 13. The clustering results of normal tissue, low-grade (I and II) , and high-grade (III and IV) glioma[45]
Fig. 15. (a) The enlarged image of the white rectangle in the Fig. 14; (b) H&E image of the same region in Fig. 15(a)[46]
Fig. 16. CARS images of astrocytoma in mouse’s brain. (a) Mosaic CARS microscopy image with low-resolution and large field of view; (b) the CARS image of local tissue of the white rectangle in (a) [46]
Fig. 17. (a) Bright field image of glioblastoma in mouse brain, with the tumor boundary outlined (black). The cyan indicates a region of interest (ROI); (b) micrograph of ROIs; (c) pseudocolour CARS image of tumor and normal brain tissues, with nuclei highlighted in blue, lipid content in red and red blood cells in green; (d) CARS image with nuclei highlighted in blue and lipid content in red; (e) CARS image with nuclei highlighted in blue, lipid content in red and CH3 stretch–CH2 in green, NB: normal brain; T: tumor cells, WM: white matter; (f) normalized CARS of different tissues[47]
Fig. 18. SRS imaging of fresh mouse tissue. (a) The myelin sheath neuron bundles of the corpus callosum in mouse brain is marked with abundant CH2 at the characteristic peak of
Fig. 19. SERS spectra of (a) healthily, (b) II grade, (c) III grade, (d) IV grade tissue[50]
Fig. 20. (a) Measurement results of Raman scattering spectra and (b) the difference in Raman spectra between neuronal glioma cells and normal astrocytes[50]
Fig. 21. Raman spectra of differentiated, undifferentiated C6 and SK-N-SH cells and normal neuronal cells[52]
Fig. 22. Difference in marker peak intensities for differentiated and undifferentiated cells. (a) C6 cells; (b) SK-N-SH cells[52]
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Mei-lan GE, Yu-ye WANG, Hai-bin LI, De-gang XU, Jian-quan YAO. Application of Raman spectroscopy in the detection of brain glioma[J]. Chinese Optics, 2024, 17(5): 995
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Received: Jan. 2, 2024
Accepted: Feb. 26, 2024
Published Online: Dec. 31, 2024
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