Rise of Raman spectroscopy in neurosurgery: a review

DePaoli, Damon; Lemoine, Émile; Ember, Katherine; Parent, Martin; Prudhomme, Michel; Cantin, Léo; Petrecca, Kevin; Leblond, Frédéric; Côté, Daniel

doi:10.1117/1.jbo.25.5.050901

Cited by 49 publications

(49 citation statements)

References 196 publications

(291 reference statements)

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“… 4 The resulting possible differentiation of tissue types opens up opportunities for its diagnostic use in surgery and pathology. 1 , 5 , 6 Therefore, Raman spectroscopy is investigated as both an intraoperative tool due to its non-invasiveness and short integration time, 6 and for its use on processed tissue. The formalin fixation process causes, however, severe changes of the spectral properties that need to be taken into account when comparing fresh and processed tissue.…”

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confidence: 99%

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma

Klamminger

Gérardy

Jelke

et al. 2021

Neuro-Oncology Advances

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Background Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. Methods To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up a SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. Results Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal appearing brain tissue can be detected. Conclusion These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As a conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.

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confidence: 99%

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma

Klamminger

Gérardy

Jelke

et al. 2021

Neuro-Oncology Advances

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“…Other similar techniques are likely on rise like diagnosing with Raman spectroscopy which has decades of research behind it [16,17], but as compared to our method it requires specialized equipment for reporting.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Thombre

Nandeesh

Vazhayil

et al. 2021

Preprint

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ObjectiveAutomated diagnosis using Artificial Intelligence (AI) techniques would be a useful addition to the intraoperative squash smear diagnosis. A robust diagnostic tool would enhance capabilities in centres where there is limited expertise for the diagnosis of intracranial lesions. The study aims to explore possibilities of deep learning technique-based models to classify squash smear images of glioma into high- and low-grade tumors.Methods500 Scanned images of squash smear were obtained intraoperatively and dataset was built. Image dataset was then pre-processed and fed into a CNN (Convolutional Neural Network) model for training and validation. The dataset consisted of 10,000 images of high (6000) and low (4000) grade gliomas, divided into three sets of training, validation and testing. ResultsCNN model based on deep learning algorithm was built and trained on training dataset to get accuracy of 96.2%. On a testing dataset which contains images previously unseen by trained model, it could achieve accuracies of 91% for diagnosing high grade glioma and 77% for low grade glioma. A positive predictive value of 86.6% and F1-score of 0.887 was achieved. Feature visualization technique was applied at the end to visualize regions of interest.ConclusionDeep Learning techniques can be applied as diagnostic tool if proper standardized images are obtained for reporting of squash smears of gliomas. The diagnostic accuracies of such tools can reach up to current standard diagnostic accuracies by conventional ways of reporting. Feature visualization techniques applied which can be used for rapid screening of slides or section of slide to assist in rapid diagnosis.

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“… 14 – 16 SRS has indeed been successfully applied to guide neurosurgery but only using excised tissues, 17 – 21 and its implementation for in vivo intraoperative image guidance has been challenging. 17 , 22 – 24 Further, unlike OCT, SRS is a point-scanning method and does not offer the convenient spatial multiplexing capability that renders OCT a fast volumetric imaging tool. Nevertheless, the unique molecular information available with SRS—particularly from proteins and lipids, which have a high Raman cross section—makes it an attractive candidate to identify tumor margins based on biochemical contrast.…”

Section: Introductionmentioning

confidence: 99%

Label-free detection of brain tumors in a 9L gliosarcoma rat model using stimulated Raman scattering-spectroscopic optical coherence tomography

et al. 2021

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Significance: In neurosurgery, it is essential to differentiate between tumor and healthy brain regions to maximize tumor resection while minimizing damage to vital healthy brain tissue. However, conventional intraoperative imaging tools used to guide neurosurgery are often unable to distinguish tumor margins, particularly in infiltrative tumor regions and low-grade gliomas.Aim: The aim of this work is to assess the feasibility of a label-free molecular imaging tool called stimulated Raman scattering-spectroscopic optical coherence tomography (SRS-SOCT) to differentiate between healthy brain tissue and tumor based on (1) structural biomarkers derived from the decay rate of signals as a function of depth and (2) molecular biomarkers based on relative differences in lipid and protein composition extracted from the SRS signals.Approach: SRS-SOCT combines the molecular sensitivity of SRS (based on vibrational spectroscopy) with the spatial and spectral multiplexing capabilities of SOCT to enable fast, spatially and spectrally resolved molecular imaging. SRS-SOCT is applied to image a 9L gliosarcoma rat tumor model, a well-characterized model that recapitulates human high-grade gliomas, including high proliferative capability, high vascularization, and infiltration at the margin. Structural and biochemical signatures acquired from SRS-SOCT are extracted to identify healthy and tumor tissues.Results: Data show that SRS-SOCT provides light-scattering-based signatures that correlate with the presence of tumors, similar to conventional OCT. Further, nonlinear phase changes from the SRS interaction, as measured with SRS-SOCT, provide an additional measure to clearly separate tumor tissue from healthy brain regions. We also show that the nonlinear phase signals in SRS-SOCT provide a signal-to-noise advantage over the nonlinear amplitude signals for identifying tumors.Conclusions: SRS-SOCT can distinguish both spatial and spectral features that identify tumor regions in the 9L gliosarcoma rat model. This tool provides fast, label-free, nondestructive, and spatially resolved molecular information that, with future development, can potentially assist in identifying tumor margins in neurosurgery.

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Rise of Raman spectroscopy in neurosurgery: a review

Cited by 49 publications

References 196 publications

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Label-free detection of brain tumors in a 9L gliosarcoma rat model using stimulated Raman scattering-spectroscopic optical coherence tomography

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