# Intraoperative Spectroscopic and Mass Spectrometric Assessment of Glioma Margins: A Systematic Review and Meta-Analysis

## TL;DR

This study reviews and compares three real-time technologies for identifying glioma tissue during surgery, finding they can improve accuracy and support safer, more precise tumor removal.

## Contribution

The paper provides a systematic review and meta-analysis comparing the diagnostic accuracy of Raman spectroscopy, mass spectrometry, and optical coherence tomography for intraoperative glioma margin assessment.

## Key findings

- Raman spectroscopy and mass spectrometry showed the highest diagnostic accuracy for tumor identification and molecular profiling.
- These technologies achieved high sensitivity and specificity, with diagnostic odds ratios indicating strong performance across multiple endpoints.
- The methods offer rapid, objective feedback during surgery without disrupting the workflow.

## Abstract

Maximal safe removal of gliomas is crucial for improving patient survival, yet surgeons often face difficulty distinguishing tumor tissue from normal brain tissue during surgery. Traditional frozen-section analysis is accurate but slow and disrupts operative workflow. This systematic review and meta-analysis evaluated three emerging, label-free intraoperative technologies—Raman spectroscopy, mass spectrometry, and optical coherence tomography—that provide real-time biochemical or structural information to guide tumor resection. By analyzing 24 human studies involving nearly 1800 patients, we found that these techniques achieve high diagnostic accuracy in identifying tumor tissue, infiltrated margins, and key molecular features such as IDH mutation status. Raman spectroscopy and mass spectrometry showed the strongest overall performance, outperforming optical coherence tomography. Importantly, these methods offer rapid, objective feedback without interrupting surgery, supporting more precise glioma resection. Our findings indicate that real-time spectroscopic and molecular diagnostics are ready for broader clinical integration and may enhance surgical decision-making in modern neuro-oncology.

Background: Maximal safe resection remains a central determinant of outcomes in glioma surgery, yet intraoperative discrimination between tumor and normal brain tissue is limited by the speed and subjectivity of frozen-section analysis. Label-free techniques such as Raman spectroscopy, mass spectrometry (MS), and optical coherence tomography (OCT) offer real-time biochemical and structural characterization that may enhance surgical precision. Their comparative diagnostic accuracy across clinically relevant endpoints has not been comprehensively evaluated. Methods: Following PRISMA 2020 guidelines, a systematic review and quantitative meta-analysis were conducted using PubMed, Embase, Scopus, and Web of Science through December 2024. Original human studies evaluating Raman, MS, or OCT for intraoperative glioma margin assessment were included. Pooled sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated using a random-effects model. Subgroup analyses addressed tumor versus normal brain tissue, infiltrated versus non-infiltrated margins, and IDH-mutant versus wild-type gliomas. Results: Twenty-four studies comprising 1768 patients met the inclusion criteria. Across all modalities, pooled sensitivity and specificity were 0.89 (95% CI 0.86–0.92) and 0.88 (95% CI 0.84–0.91), with a pooled DOR of 65.7 (95% CI 42.3–101.8; logDOR 4.18), indicating high overall discriminative performance. Tumor versus normal differentiation achieved DOR 72.4 (logDOR 4.28; I2 = 26%), infiltrated margin detection DOR 41.8 (logDOR 3.73; I2 = 41%), and IDH classification DOR 52.3 (logDOR 3.96; I2 = 29%). No publication bias was observed. Raman and MS outperformed OCT. Conclusions: Raman spectroscopy, mass spectrometry, and OCT demonstrate strong diagnostic accuracy for real-time intraoperative glioma evaluation, enabling reliable tissue differentiation and molecular profiling that may enhance resection extent and support precision, molecularly informed neurosurgery.