Raman Spectroscopy for Glioma Diagnosis Toward Precision Neuro-Oncology: A Systematic Review
In brief
Raman tools showed strong glioma tissue discrimination across 47 studies
A review of 47 studies found Raman-based technologies consistently distinguished glioma tissue, assessed tumor margins and supported molecular characterization, including during surgery. But varied methods, mostly retrospective single-center research and limited external validation leave their performance in broader clinical practice uncertain; prospective multicenter studies are needed before routine use.
- Journal
- Critical reviews in oncology/hematology (Q1)
- Published
- 31 October 2026
- Study design
- Systematic review of cohort studies
- Evidence level
- Level 2, Moderate (CEBM 2a)
- Authors
- Corneliu Ovidiu Vrancianu, Marian Necula, Eduard Milea, Nicoleta Siminea, Roxana-Elena Cristian, Alice Stoica, et al.
- PMID
- 42966518
- DOI
- 10.1016/j.critrevonc.2026.105675
Why clinicians should know about it
- Picked for Histology (top studies of the week, 1 November 2026): Reviews Raman-based optical tissue imaging and histology methods
Abstract
INTRODUCTION: Gliomas are the most common primary malignant tumors of the central nervous system and remain difficult to manage because of diffuse infiltration and molecular heterogeneity. As glioma classification increasingly relies on molecular biomarkers, interest has grown in label-free technologies capable of providing real-time biochemical information during surgery. Although Raman spectroscopy has emerged as a promising optical approach, its diagnostic performance and translational readiness remain incompletely defined. METHODS: This systematic review was registered in PROSPERO and conducted according to PRISMA 2020 guidelines. PubMed, Embase, Scopus, and Web of Science were searched for studies published between 2015 and 2025 evaluating Raman-based technologies for glioma diagnosis. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment using QUADAS-2. Owing to methodological heterogeneity, findings were synthesized using a SWiM-guided narrative approach. RESULTS: Forty-seven studies met the eligibility criteria. Across conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, stimulated Raman histology, stimulated Raman scattering, and multimodal platforms, Raman-based technologies consistently demonstrated strong performance for tissue discrimination, tumor margin assessment, molecular characterization, and intraoperative tissue evaluation. Recent studies increasingly integrated artificial intelligence for automated tissue classification and molecular prediction. However, substantial heterogeneity in acquisition protocols, analytical workflows, and validation strategies, together with predominantly retrospective single-center studies and limited external validation, reduced confidence in clinical generalizability. QUADAS-2 identified the greatest risk of bias in the patient-selection and index-test domains. CONCLUSIONS: Raman spectroscopy is evolving from an optical imaging technique into a molecular decision-support platform for precision neuro-oncology. Future clinical translation will depend on methodological standardization, prospective multicenter validation, explainable artificial intelligence, and integration into routine neurosurgical workflows.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.