Department of Neurosurgery, The University of Texas at Austin Dell Medical School, Austin, Texas, USA.
Department of Neurosurgery, The University of Texas at Austin Dell Medical School, Austin, Texas, USA.
World Neurosurg. 2024 Sep;189:26-32. doi: 10.1016/j.wneu.2024.05.112. Epub 2024 May 23.
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 (I = 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 (I = 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.
术中拉曼光谱（RS）已被确定为外科医生快速、无创地区分病变组织和正常组织的潜在工具。由于该主题的上一次荟萃分析发表于 2016 年，因此，用于处理光谱的光谱仪设备和机器学习模型的改进可能导致 RS 功效的提高。因此，我们决定进行一项荟萃分析，以确定 RS 在区分脑胶质瘤组织和正常脑组织方面的效果。在进行这项荟萃分析时，我们遵循了系统评价和荟萃分析的首选报告项目的指南。在 PubMed 和 Web of Science 上进行了检索，以查找 2016 年至 2022 年期间使用术中 RS 和标准组织学方法来区分脑胶质瘤和正常脑组织的前瞻性和回顾性研究。使用 JASP 中的随机效应模型和受限极大似然估计对对数优势比、灵敏度和特异性进行荟萃分析。共有 9 项研究符合纳入标准，包括 673 名患者和 8319 个拉曼光谱。对对数诊断优势比的荟萃分析显示存在高度异质性（I=79.83%），并产生了一个转换后的诊断优势比为 76.71（95%置信区间：39.57-148.71）。最后，对 RS 用于脑胶质瘤组织的灵敏度和特异性的荟萃分析显示存在高度异质性（分别为 I=99.37%和 98.21%），并产生了总体灵敏度为 95.3%（95%置信区间：91.0%-99.6%）和总体特异性为 71.2%（95%置信区间：54.8%-87.6%）。计算综合接收者操作特征曲线得出的总体曲线下面积为 0.9265。拉曼光谱代表了外科医生快速、准确地区分健康脑组织和脑胶质瘤组织的一种很有前途的工具。
