Glioma is the most common primary brain tumor, demanding prompt and accurate diagnosis to guide therapy decisions. Conventional diagnostic methods such as histopathology and neuroimaging are limited by their invasiveness, subjectivity, or lack of intraoperative precision. Raman spectroscopy is an emerging, label-free optical technique that detects the unique molecular "fingerprint" of tissues, enabling real-time differentiation between tumor-infiltrated and normal brains based on their biochemical composition. Despite a growing body of experimental and translational studies, a comprehensive synthesis of Raman spectroscopy's clinical applicability in detecting glioma micro-infiltration is lacking.

We will search four electronic databases (PubMed, Embase, Scopus, and Web of Science) for English-language studies published from 2015 to 2025. Eligible studies may use in vivo Raman (subjects with suspected or histologically confirmed glioma), ex vivo Raman (freshly excised human tissue), or in vitro analysis on archived human samples, provided the tissue originated from confirmed glioma cases. Primary outcomes will be diagnostic accuracy measures, including but not limited to sensitivity, specificity, accuracy, area under de curve (AUC), positive predictive value (PPV), negative predictive value (NPV), diagnostic odds ratio (DOR), and positive/negative likelihood ratios (LR+/LR−). Secondary outcomes include its role in intraoperative margin assessment, diagnostic time, and machine learning-assisted classification. Data traction and risk of bias assessment will be conducted independently by two reviewers. Meta-analyses will be performed using specific statistics where applicable. If a meta-analysis is not feasible, a structured narrative synthesis will be employed, following the SWiM (Synthesis Without Meta-analysis) guidelines. GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) will be applied to evaluate the certainty of evidence. The review will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.

This review will synthesize the emerging diagnostic landscape of Raman spectroscopy for glioma, positioning it as a transformative adjunct to traditional histopathology and intraoperative decision-making. Our findings will pave the way for a translational roadmap to integrate real-time Raman spectroscopy into neurosurgical workflows with machine learning support.