Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA–induced fluorescence-guided surgery

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  • 1 Nuffield Department of Clinical Neurosciences, and
  • 2 Nuffield Department of Surgery, University of Oxford, John Radcliffe Hospital, Oxford;
  • 3 Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford;
  • 4 Renishaw plc, Spectroscopy Products Division, Gloucestershire;
  • 5 Department of Chemistry, University of Oxford; and
  • 6 FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
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OBJECTIVE

Raman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)–induced fluorescence.

METHODS

A principal component analysis (PCA)–fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA–induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor.

RESULTS

The PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA–induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA–induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009).

CONCLUSIONS

Raman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA–induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA–induced fluorescence to guide extent of resection in glioma surgery.

ABBREVIATIONS 5-ALA = 5-aminolevulinic acid; CI = confidence interval; EMSC = extended multiplicative scatter correction; IDH = isocitrate hydrogenase; IHC = immunohistochemistry; LDA = linear discriminant analysis; PC = principal component; PCA = PC analysis.

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Contributor Notes

Correspondence Laurent J. Livermore: Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK. james.livermore@ouh.nhs.uk.

INCLUDE WHEN CITING Published online October 2, 2020; DOI: 10.3171/2020.5.JNS20376.

Disclosures Spectroscopic instrumentation and data analysis software were loaned by Renishaw plc. M.I. and I.M.B. are full-time employees of Renishaw spectroscopic division. Renishaw did not provide any additional funding for the project or individually to the authors.

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