Williams Ari, Ordaz Josue D, Budnick Hailey, Desai Virendra R, Tailor Jignesh, Raskin Jeffrey S
Indiana University School of Medicine, Indianapolis, Indiana, USA.
Department of Neurological Surgery, Section of Pediatric Neurosurgery, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Oper Neurosurg. 2023 Sep 1;25(3):269-277. doi: 10.1227/ons.0000000000000764. Epub 2023 May 22.
Robot-assisted stereoelectroencephalography (sEEG) is steadily supplanting traditional frameless and frame-based modalities for minimally invasive depth electrode placement in epilepsy workup. Accuracy rates similar to gold-standard frame-based techniques have been achieved, with improved operative efficiency. Limitations in cranial fixation and placement of trajectories in pediatric patients are believed to contribute to a time-dependent accumulation of stereotactic error. Thus, we aim to study the impact of time as a marker of cumulative stereotactic error during robotic sEEG.
All patients between October 2018 and June 2022 who underwent robotic sEEG were included. Radial errors at entry and target points as well as depth and Euclidean distance errors were collected for each electrode, excluding those with errors over 10 mm. Target point errors were standardized by planned trajectory length. ANOVA and error rates over time were analyzed using GraphPad Prism 9.
Forty-four patients met inclusion criteria for a total of 539 trajectories. Number of electrodes placed ranged from 6 to 22. Average root mean squared error was 0.45 ± 0.12 mm. Average entry, target, depth, and Euclidean distance errors were 1.12 ± 0.41 mm, 1.46 ± 0.44 mm, -1.06 ± 1.43 mm, and 3.01 ± 0.71 mm, respectively. There was no significant increased error with each sequential electrode placed (entry error P -value = .54, target error P -value = .13, depth error P -value = .22, Euclidean distance P -value = .27).
No decremental accuracy over time was observed. This may be secondary to our workflow which prioritizes oblique and longer trajectories first and then into less error-prone trajectories. Further study on the effect of level of training may reveal a novel difference in error rates.
在癫痫检查中，机器人辅助立体定向脑电图（sEEG）正逐渐取代传统的无框架和有框架方式，用于微创深度电极植入。已实现与基于框架的金标准技术相似的准确率，且手术效率有所提高。小儿患者颅骨固定和轨迹放置方面的局限性被认为会导致立体定向误差随时间累积。因此，我们旨在研究时间作为机器人sEEG期间累积立体定向误差指标的影响。
纳入2018年10月至2022年6月期间接受机器人sEEG的所有患者。收集每个电极在进入点和靶点的径向误差以及深度和欧几里得距离误差，排除误差超过10毫米的电极。靶点误差通过计划轨迹长度进行标准化。使用GraphPad Prism 9分析方差分析和随时间的误差率。
44例患者符合纳入标准，共539条轨迹。植入电极数量为6至22个。平均均方根误差为0.45±0.12毫米。平均进入点、靶点、深度和欧几里得距离误差分别为1.12±0.41毫米、1.46±0.44毫米、-1.06±1.43毫米和3.01±0.71毫米。随着每个连续电极的放置，误差没有显著增加（进入点误差P值 = 0.54，靶点误差P值 = 0.13，深度误差P值 = 0.22，欧几里得距离P值 = 0.27）。
未观察到随时间准确性下降。这可能是由于我们的工作流程优先选择倾斜和较长的轨迹，然后是误差较小的轨迹。对培训水平影响的进一步研究可能会揭示误差率的新差异。
