Department of Neurosurgery, King's College Hospital NHS Foundation Trust, Denmark Hill, London SE5 9RS, United Kingdom.
Department of Clinical Neurophysiology, King's College Hospital NHS Foundation Trust, Denmark Hill, London SE5 9RS, United Kingdom.
Cereb Cortex. 2024 Jan 14;34(1). doi: 10.1093/cercor/bhad493.
Developing neurophysiological tools to predict WHO tumor grade can empower the treating teams for a better surgical decision-making process. A total of 38 patients with supratentorial diffuse gliomas underwent an asleep-awake-sedated craniotomies for tumor removal with intraoperative neuromonitoring. The resting motor threshold was calculated for different train stimulation paradigms during awake and asleep phases. Receiver operating characteristic analysis and Bayesian regression models were performed to analyze the prediction of tumor grading based on the resting motor threshold differences. Significant positive spearman correlations were observed between resting motor threshold excitability difference and WHO tumor grade for train stimulation paradigms of 5 (R = 0.54, P = 0.00063), 4 (R = 0.49, P = 0.002), 3 (R = 0.51, P = 0.001), and 2 pulses (R = 0.54, P = 0.0007). Kruskal-Wallis analysis of the median revealed a positive significant difference between the median of excitability difference and WHO tumor grade in all paradigms. Receiver operating characteristic analysis showed 3 mA difference as the best predictor of high-grade glioma across different patterns of motor pathway stimulation. Bayesian regression found that an excitability difference above 3 mA would indicate a 75.8% probability of a glioma being high grade. Our results suggest that cortical motor excitability difference between the asleep and awake phases in glioma surgery could correlate with tumor grade.
开发神经生理工具来预测 WHO 肿瘤分级可以为治疗团队提供更好的手术决策过程。共有 38 例幕上弥漫性脑胶质瘤患者接受了术中神经监测的清醒-睡眠-镇静开颅切除术。在清醒和睡眠阶段,计算了不同训练刺激模式的静息运动阈值。进行了接收器操作特征分析和贝叶斯回归模型分析,以根据静息运动阈值差异预测肿瘤分级。对于 5 个(R = 0.54,P = 0.00063)、4 个(R = 0.49,P = 0.002)、3 个(R = 0.51,P = 0.001)和 2 个脉冲(R = 0.54,P = 0.0007)的训练刺激模式,观察到静息运动阈值兴奋性差异与 WHO 肿瘤分级之间存在显著正 Spearman 相关性。Kruskal-Wallis 分析中位数显示,在所有模式下,兴奋性差异的中位数与 WHO 肿瘤分级之间存在正显著差异。接收器操作特征分析显示,在不同的运动通路刺激模式下,3 mA 的差异是预测高级别胶质瘤的最佳指标。贝叶斯回归发现,兴奋性差异大于 3 mA 时,表明胶质瘤为高级别的概率为 75.8%。我们的研究结果表明,胶质瘤手术中睡眠和清醒阶段皮质运动兴奋性差异可能与肿瘤分级相关。