Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis

The Value of SINO Robot and Angio Render Technology for Stereoelectroencephalography Electrode Implantation in Drug-Resistant Epilepsy

BACKGROUND

 Stereoelectroencephalography (SEEG) electrodes are implanted using a variety of stereotactic technologies to treat refractory epilepsy. The value of the SINO robot for SEEG electrode implantation is not yet defined. The aim of the current study was to assess the value of the SINO robot in conjunction with Angio Render technology for SEEG electrode implantation and to assess its efficacy.

METHODS

 Between June 2018 and October 2020, 58 patients underwent SEEG electrode implantation to resect or ablate their epileptogenic zone (EZ). The SINO robot and the Angio Render technology was used to guide the electrodes and visualize the individual vasculature in a three-dimensional (3D) fashion. The 3D view functionality was used to increase the safety and accuracy of the electrode implantation, and for reducing the risk of hemorrhage by avoiding blood vessels.

RESULTS

 In this study, 634 SEEG electrodes were implanted in 58 patients, with a mean of 10.92 (range: 5-18) leads per patient. The mean entry point localization error (EPLE) was 0.94 ± 0.23 mm (range: 0.39-1.63 mm), and the mean target point localization error (TPLE) was 1.49 ± 0.37 mm (range: 0.80-2.78 mm). The mean operating time per lead (MOTPL) was 6. 18 ± 1.80 minutes (range: 3.02-14.61 minutes). The mean depth of electrodes was 56.96 ± 3.62 mm (range: 27.23-124.85 mm). At a follow-up of at least 1 year, in total, 81.57% (47/58) patients achieved an Engel class I seizure freedom. There were two patients with asymptomatic intracerebral hematomas following SEEG electrode placement, with no late complications or mortality in this cohort.

CONCLUSIONS

 The SINO robot in conjunction with Angio Render technology-in SEEG electrode implantation is safe and accurate in mitigating the risk of intracranial hemorrhage in patients suffering from drug-resistant epilepsy.

Real-time display of intracranial subdural electrodes and the brain surface during an electrode implantation procedure using permeable film

Background

Subdural electrode (SDE) implantation is an important method of diagnosing epileptogenic lesions and mapping brain function, even with the current preference for stereoelectroencephalography. We developed a novel method to assess SDEs and the brain surface during the electrode implantation procedure using brain images printed onto permeable films and intraoperative fluoroscopy. This method can help verify the location of the electrode during surgery and improve the accuracy of SDE implantation.

Methods

We performed preoperative imaging by magnetic resonance imaging and computed tomography. Subsequently, the images were edited and fused to visualize the gyrus and sulcus better. We printed the images on permeable films and superimposed them on the intraoperative fluoroscopy display. The intraoperative and postoperative coordinates of the electrodes were obtained after the implantation surgery, and the differences in the locations were calculated.

Results

Permeable films were created for a total of eight patients with intractable epilepsy. The median difference of the electrodes between the intraoperative and postoperative images was 4.6 mm (Interquartile range 2.9-7.1). The locations of electrodes implanted outside the operation field were not significantly different from those implanted inside.

Conclusion

Our new method may guide the implantation of SDEs into their planned location.

An automated algorithm for stereoelectroencephalography electrode localization and labelling

PURPOSE

Stereoelectroencephalography (sEEG) is increasingly utilized for localization of seizure foci, functional mapping, and neurocognitive research due to its ability to target deep and difficult to reach anatomical locations and to study in vivo brain function with a high signal-to-noise ratio. The research potential of sEEG is constrained by the need for accurate localization of the implanted electrodes in a common template space for group analyses.

METHODS

We present an algorithm to automate the grouping of sEEG electrodes by trajectories, labelled by target and insertion point. This algorithm forms the core of a pipeline that fully automates the entire process of electrode localization in standard space, using raw CT and MRI images to produce atlas labelled MNI coordinates.

RESULTS

Across 196 trajectories from 20 patients, the pipeline successfully processed 190 trajectories with localizations within 0.25±0.55 mm of the manual annotation by two reviewers. Six electrode trajectories were not directly identified due to metal artifacts and locations were interpolated based on the first and last contact location and the number of contacts in that electrode as listed in the surgical record.

CONCLUSION

We introduce our algorithm and pipeline for automatically localizing, grouping, and classifying sEEG electrodes from raw CT and MRI. Our algorithm adds to existing pipelines and toolboxes for electrode localization by automating the manual step of marking and grouping electrodes, thereby expedites the analyses of sEEG data, particularly in large datasets.

The Association Between Trajectory-Skull Angle and Accuracy of Stereoelectroencephalography Electrode Implantation in Drug-Resistant Epilepsy

OBJECTIVE

To analyze the relationship between trajectory-skull angle and stereoelectroencephalography electrode implantation accuracy in drug-resistant epilepsy patients, aiming to guide clinical electrode placement and enhance surgical precision and safety.

METHODS

We conducted a retrospective analysis of medical records and surgical characteristics of 32 consecutive patients diagnosed with drug-resistant epilepsy, who underwent stereoelectroencephalography procedures at our center from June 2020 to June 2023. To evaluate the accuracy of electrode implantation, we utilized preoperative and postoperative computed tomography scans fused with SinoPlan software-planned trajectories. Entry radial error and target vector error were assessed as measurements of electrode implantation accuracy.

RESULTS

After adjusting for confounders, we found a significant positive correlation between trajectory-skull angle and entry radial error (β = 0.02, 95% CI: 0.01-0.03, P < 0.001). Likewise, a significant positive correlation existed between trajectory-skull angle and target vector error in all three models (β = 0.03, 95% CI: 0.01-0.04, P < 0.001). Additionally, a U-shaped relationship between trajectory-skull angle and target vector error was identified using smooth curve fitting. This U-shaped pattern persisted in both frame-based and robot-guided stereotactic techniques. According to the two-piecewise linear regression model, the inflection points were 9° in the frame-based group and 16° in the robot-guided group.

CONCLUSIONS

This study establishes a significant positive linear correlation between trajectory-skull angle and entry radial error, along with a distinctive U-shaped pattern in the relationship between trajectory-skull angle and target vector error. Our findings suggest that trajectory-skull angles of 9° (frame-based) and 16° (robot-guided) may optimize the accuracy of target vector error.

Implantation accuracy of novel polyimide stereotactic electroencephalographic depth electrodes-a human cadaveric study

Introduction

Stereoelectroencephalography (sEEG) is a minimally invasive procedure that uses depth electrodes stereotactically implanted into brain structures to map the origin and propagation of seizures in epileptic patients. Implantation accuracy of sEEG electrodes plays a critical role in the safety and efficacy of the procedure. This study used human cadaver heads, simulating clinical practice, to evaluate (1) neurosurgeon's ability to implant a new thin-film polyimide sEEG electrode according to the instructions for use (IFU), and (2) implantation accuracy.

Methods

Four neurosurgeons (users) implanted 24 sEEG electrodes into two cadaver heads with the aid of the ROSA robotic system. Usability was evaluated using a questionnaire that assessed completion of all procedure steps per IFU and user errors. For implantation accuracy evaluation, planned electrode trajectories were compared with post-implantation trajectories after fusion of pre- and postoperative computer tomography (CT) images. Implantation accuracy was quantified using the Euclidean distance for entry point error (EPE) and target point error (TPE).

Results

All sEEG electrodes were successfully placed following the IFU without user errors, and post-implant survey of users showed favorable handling characteristics. The EPE was 1.28 ± 0.86 mm and TPE was 1.61 ± 0.89 mm. Long trajectories (>50 mm) had significantly larger EPEs and TPEs than short trajectories (<50 mm), and no differences were found between orthogonal and oblique trajectories. Accuracies were similar or superior to those reported in the literature when using similar experimental conditions, and in the same range as those reported in patients.

Discussion

The results demonstrate that newly developed polyimide sEEG electrodes can be implanted as accurately as similar devices in the marker without user errors when following the IFU in a simulated clinical environment. The human cadaver ex-vivo test system provided a realistic test system, owing to the size, anatomy and similarity of tissue composition to that of the live human brain.

Robotic-Assisted Stereoelectroencephalography: A Systematic Review and Meta-Analysis of Safety, Outcomes, and Precision in Refractory Epilepsy Patients

Robotic assistance in stereoelectroencephalography (SEEG) holds promising potential for enhancing accuracy, efficiency, and safety during electrode placement and surgical procedures. This systematic review and meta-analysis, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and International Prospective Register of Systematic Reviews (PROSPERO) registration, delves into the latest advancements and implications of robotic systems in SEEG, while meticulously evaluating outcomes and safety measures. Among 855 patients suffering from medication-refractory epilepsy who underwent SEEG in 29 studies, averaging 24.6 years in age, the most prevalent robots employed were robotic surgical assistant (ROSA) (450 patients), Neuromate (207), Sinovation (140), and ISys1 (58). A total of 8,184 electrodes were successfully implanted, with an average operative time of 157.2 minutes per procedure and 15.1 minutes per electrode, resulting in an overall mean operative time of 157.7 minutes across all studies. Notably, the mean target point error (TPE) stood at 2.13 mm, the mean entry point error (EPE) at 1.48 mm, and postoperative complications occurred in 7.69% of robotically assisted (RA) SEEG cases (60), with 85% of these complications being asymptomatic. This comprehensive analysis underscores the safety and efficacy of RA-SEEG in patients with medication-refractory epilepsy, characterized by low complication rates, reduced operative time, and precise electrode placement, supporting its widespread adoption in clinical practice, with no discernible differences noted among the various robotic systems.