Meta-Analysis of the Efficacy of Raman Spectroscopy and Machine-Learning-Based Identification of Glioma Tissue

Intraoperative Raman spectroscopy (RS) has been identified as a potential tool for surgeons to rapidly and noninvasively differentiate between diseased and normal tissue. Since the previous meta-analysis on the subject was published in 2016, improvements in both spectroscopy equipment and machine learning models used to process spectra may have led to an increase in RS efficacy. Therefore, we decided to conduct a meta-analysis to determine the efficacy of RS when differentiating between glioma tissue and normal brain tissue. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed while conducting this meta-analysis. A search was conducted on PubMed and Web of Science for prospective and retrospective studies published between 2016 and 2022 using intraoperative RS and standard histology methods to differentiate between glioma and normal brain tissue. Meta-analyses of log odds ratios, sensitivity, and specificity were conducted in JASP using the random-effects model with restricted maximum likelihood estimation. A total of 9 studies met our inclusion criteria, comprising 673 patients and 8319 Raman spectra. Meta-analysis of log diagnostic odds ratios revealed high heterogeneity (I² = 79.83%) and yielded a back-transformed diagnostic odds ratio of 76.71 (95% confidence interval: 39.57-148.71). Finally, meta-analysis for sensitivity and specificity of RS for glioma tissue showed high heterogeneity (I² = 99.37% and 98.21%, respectively) and yielded an overall sensitivity of 95.3% (95% confidence interval: 91.0%-99.6%) and an overall specificity of 71.2% (95% confidence interval: 54.8%-87.6%). Calculation of a summary receiver operating curve yielded an overall area under the curve of 0.9265. Raman spectroscopy represents a promising tool for surgeons to quickly and accurately differentiate between healthy brain tissue and glioma tissue.