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Review
. 2023 Jan 5:12:1086643.
doi: 10.3389/fonc.2022.1086643. eCollection 2022.

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

Yi Zhang  1 , Hongquan Yu  1 , Yunqian Li  1 , Haiyang Xu  1 , Liu Yang  1 , Peilin Shan  1 , Yuejiao Du  1 , Xiaokai Yan  1 , Xuan Chen  1
Affiliations
Review

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

Yi Zhang et al. Front Oncol. .

Abstract

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

Keywords: EOR; Raman spectroscopy; SERS; SRH; glioma; intraoperative.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
An example of Raman spectroscopy shows normalized mean spectra with standard deviation between healthy (blue) and tumor patients (red). Arrows mark the new Raman peaks about glioma identified by the researcher (28).
Figure 2
Figure 2
A Raman spectroscopy study examines freshly collected ex vivo brain tissue and achieves good results with a predictive model constructed by machine learning algorithms. (A) samples of size 2-4 mm in length, width, and height are needed. (B) Selection of sites for Raman spectroscopy. (C) ROC curve of a trained logistic regression model to identify tumor and normal brain tissue. (D) ROC curve of a trained logistic regression model to identify low grade glioma and normal brain tissue (30).
Figure 3
Figure 3
The researchers used CNN to construct the prediction model by SRH images. (A) The prediction results of the SRH-based prediction model are demonstrated. (B) SRH images and CNN probability heatmaps. The model was able to correctly identify pseudoprogression, tumor recurrence, and infiltrative glioma. Scale bars = 50 μm (52).
Figure 4
Figure 4
A hand-held fiber optic probe consists of a laser emitter connected to a spectral detector, with data acquisition ultimately controlled by a PC. The illumination and detection light paths are spatially coincident. When the probe performs measurements, it is in direct contact with the brain at the resection edge (53).
Figure 5
Figure 5
(A) Results of one study for IDH-WT and IDH-MUT, with arrows representing the most discriminant peaks with a known biological assignment. (B–E) are the 3-group model and 2-group model, respectively, of another study for molecular typing of gliomas based on Raman spectroscopy results. (TPR = true positive rate; FPR = false positive rate; Astro MUT = Astroglial tumor isocitrate dehydrogenase IDH-mutant; Astro WT = Astroglial tumor, IDH-wild-type; Oligo = Oligodendroglioma) (14, 55).
See this image and copyright information in PMC

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