Raman spectroscopy: a novel tool for intraoperative guidance in surgical neuro-oncology. (31st January 2018)

• Record Type: Journal Article
• Authors: Vaqas, Babar; Patel, Imran; Short, Michael; Zeng, Haishan; O'Neill, Kevin
• Is Part Of: Neuro-oncology. Volume 20(2018)Supplement 1
• Page Start: i16
• Page End: i16
• Publication Date: 2018-01-31
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/nox238.069
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: INTRODUCTION: Raman spectroscopy offers an optical label-free and non destructive real-time solution for tissue identification and surgical guidance during Neurosurgery for brain tumours. We evaluate a novel fibre optic point-measurement system during its first use during surgery in a consecutive series of 30 patients with a range of brain tumors. METHODS: Raman spectroscopy was deployed using a surgical protocol in this single centre study of 30 consecutive craniotomies for tumor. Analysis of tissue was done in-vivo during surgery at the brain surface, on route to the tumor and in superficial and deep tumor zones. Sampling was also performed along the resection margin and all spectra were compared to matched core biopsy samples verified by routine histopathology. Ex-vivo tissue analysis was also performed at different time points to look at change in tissue signal characteristics over time. RESULTS: Over 3, 000 Raman spectra from the brain were recorded. Multivariate analysis using PCA/LDA was performed. Most easily identified tumors include meningiomas and metastases. Low grade and high grade gliomas were also identified with high accuracy. CONCLUSIONS: Raman spectroscopy represents a new modality for in-vivo tissue characterisation with high accuracy. It has the ability to rapidly recognise healthy brain tissue and tumour tissue and could be a valuable tool aiding complete resection of low grade lesions in eloquent brain regions. Current challenges to more widespread adoption include the modifications necessary for it to work in the OR and patient-patient and machine-machine variability.