Epilepsy has been defined as “a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures.”10 Epilepsy can have wide-ranging effects on a patient’s quality of life and can result in physical injury, psychosocial dysfunction, cognitive decline, and risk of death.14 One-third of patients with epilepsy continue to have seizures despite their use of 2 or more appropriately prescribed antiepileptic drug schedules. These patients are defined as having drug-resistant epilepsy.25 Surgical intervention can potentially cure drug-resistant epilepsy if the region from which the seizures arise, known as the epileptogenic zone (EZ), can be identified and removed safely. A patient’s chances of achieving sustained freedom from seizures after epilepsy surgery are highest when the seizure semiology, electrophysiological investigations, imaging findings, and neuropsychological assessment are concordant. In such cases, the patient does not require any further imaging or testing unless there is proximity of the suspected EZ to eloquent cortex and resective surgery can be performed. In a proportion of patients, results of the noninvasive presurgical evaluation are not clear or discordant, and invasive intracranial EEG recordings, in the form of either grid/strip implantation or stereoelectroencephalography (SEEG), are required. SEEG involves the stereotactic placement of multiple (8–16) electrodes at predefined regions of the brain to help delineate the EZ and the spatial and temporal seizure-network spread within the brain. A recent meta-analysis regarding the safety of electrode implantation for SEEG found the overall risk of complications to be 1.3% per patient. The greatest risk related to electrode placement is intracranial hemorrhage, which had a pooled prevalence of 1% per patient.15 The factors that determine the risk of hemorrhage are the initial planned trajectory and the accuracy of the implantation method. The methods currently used to implant electrodes for SEEG involve stereotactic frame-based, frameless, and robotic systems. There is a paucity of evidence in the literature from comparisons of these methods performed to determine which one is the most accurate, but entry and target point accuracies have ranged from 0.78 to 3.5 and 1.70 to 3.66 mm, respectively.28
Electrode trajectories currently are planned manually to sample the regions of interest (ROIs) while maximizing gray matter contact and distance from blood vessels. This task is time-consuming and requires significant multidisciplinary input. We previously described the benefits of multimodal 3D imaging for manual electrode planning and an early version of computer-assisted planning (CAP).17,18 In the initial study, manually planned electrode-implantation schemes for 18 patients (166 electrodes) were recreated retrospectively using EpiNav software. An earlier version of the software required the target points for the electrodes to be placed manually on the MR image, and the software then would calculate the safest electrode trajectory based on the cumulative distance from segmented blood vessels along the whole trajectory.18 The computer-generated and manually determined trajectories then were rated by 3 independent, blinded neurosurgeons as to whether they were feasible for implantation. Overall, the computer-generated electrodes resulted in significantly shorter intracranial length, increased distance from blood vessels, greater gray matter sampling, and improved drilling angles (p < 0.05 for all parameters). Of the computer-generated electrodes, 78.9% were deemed feasible for implantation by at least 2 of the 3 independent neurosurgeons.
Further development of the EpiNav software implemented its ability to define entry and target zones constrained by anatomical structures.24 Users can now define an ROI by typing or clicking on an anatomical location (e.g., right amygdala) and allowing the computer algorithm to define the safest entry and target points within the anatomical structure as a whole. Furthermore, multiple trajectories can be placed within the same anatomical structure, and electrodes will be spread evenly within safe zones to maximize region sampling. This ability is of particular benefit for large anatomical targets, such as the cingulate cortex, and when high-density sampling of a structure such as the insula or hippocampus is required. We confirmed external validity of the generated electrodes from 5 independent, blinded epilepsy neurosurgeons, from outside institutions, who had expertise in implanting electrodes for SEEG and none of whom were involved in generation of the initial manually determined plans. To gauge surgeon variability and preferences, we assessed why surgeons rated trajectories as infeasible. The implantation methods used
Methods
Patients
We included 13 consecutive patients who underwent manually determined planning of electrode placement and surgical implantation between July 2015 and October 2016. Informed consent was obtained from all patients before their inclusion in the study. The National Research Ethics Service Committee London approved this study. Patient demographics are summarized in Table 1.
The case of each patient had been discussed by a multidisciplinary team (MDT) that consisted of epileptologists, neurosurgeons, neuropsychologists, neuropsychiatrists, and neuroradiologists. From the noninvasive presurgical evaluation, the team agreed on the hypothesized EZ and determined the requirement for invasive EEG recording. Patients who required subdural grid implantation were excluded from the study. Members of the MDT also agreed on regions for sampling for SEEG and generated a list of brain regions that required sampling. Before final approval by the MDT, manual plans were then created by a consulting neurosurgeon who had subspecialty expertise in epilepsy surgery.
Multimodal Imaging
MRI was performed on a GE 3-T MR750 scanner with a 32-channel head coil. A coronal 3D T1-weighted magnetization-prepared rapid-acquisition gradient echo scan was performed with a field of view (FOV) of 224 × 256 × 256 mm (anterior to posterior, left to right, inferior to superior, respectively) and an acquisition matrix of 224 × 256 × 256, for a voxel size of 1-mm isotropic resolution (TE/TR/TI 3.1/7.4/400 msec; flip angle 11°; parallel imaging acceleration factor 2). 3D FLAIR scans were acquired with a 3D fast–spin echo sequence with variable flip-angle readout (CUBE) with the same FOV and acquisition matrix, for a 1-mm isotropic resolution (TR/TI/TE 6200/1882/137 msec; echo train length 150; parallel imaging acceleration 2 [along both the in-plane and through-plane phase-encoding axes]). Vascular imaging comprised postgadolinium T1-weighted and phase-contrast MR angiography (MRA) and MR venography (MRV) scans. The axial postgadolinium T1-weighted scan was acquired with a fast spoiled gradient echo sequence with a FOV of 256 × 256 × 224 mm and an acquisition and reconstruction matrix of 256 × 256 × 224 (TE/TR 3.1/7.4 msec; flip angle 11°). MRA and MRV were performed using a 3D phase-contrast sequence with a FOV of 220 × 220 × 148.8 mm and an acquisition matrix of 384 × 256 × 124, for a reconstructed voxel size of 0.43 × 0.43 × 0.60 mm (flip angle 8°; parallel imaging acceleration factor 2). To highlight the arteries, MRA was performed with a velocity encoding of 80 cm/second (TE/TR 4.0/9.3 msec). For sensitivity to the venous circulation, the MRV was performed with a velocity encoding of 15 cm/second (TE/TR 4.8/26.4 msec), fat suppression, and a saturation band inferior to the FOV.
Manual Planning
Manual plans were generated using volumetric T1-weighted gadolinium-enhanced images as the reference image on which MRV images were coregistered, and vessels were extracted using a previously described tensor voting framework algorithm.30 Entry and target points were placed manually using axial, coronal, and sagittal reconstructions, and trajectories were checked using the “probe’s-eye” function. A 3D model of the cortical surface was used to ensure that entry points were on the crown of gyri.
EpiNav
Data Processing and Model Generation
EpiNav is a software platform that allows multimodal image coregistration, vessel segmentation, 3D model generation, and manual and automated electrode planning. T1-weighted magnetization-prepared rapid-acquisition gradient echo sequences were submitted for whole-brain parcellation (geodesic information flows) from which cortical, gray matter, and sulcal models were generated.6,20 Preoperative CT scans were used to generate skull models, which then were modified to prevent entry through the contralateral hemisphere, face, ear, posterior fossa, and skull base.
The technical aspects of the CAP algorithm used in this study were described previously.23 In brief, the user defines target points as ROIs for electrode sampling, which can be done by typing the name of a structure (e.g., right amygdala) or clicking on the ROI of the brain-parcellation image. The entry ROI can be specified if a superficial target is also required (e.g., entry through the motor cortex to target the supplementary motor area), but it is not obligatory. In this study, the same target points and, if specified, entry points were selected based on the requirements of the SEEG MDT planning meeting. The user defines a maximum electrode length (90 mm was applied for all electrodes) and a maximum drilling angle (25° orthogonal to the skull). The CAP algorithm then removes any potential electrode trajectories that do not adhere to length and angle constraints before ensuring that the trajectories pass through the skull model to the target ROI. If an entry ROI is defined, trajectories that do not pass through this ROI will be removed also. Then, the remaining trajectories are checked to ensure that they do not collide with a critical structure such as a blood vessel or sulcus. A minimum distance from vessels can be set as a safety margin by the user (3 mm was used for all electrodes in this study). The electrode trajectories that satisfy the requirements are then stratified based on risk, which is calculated as a function of the cumulative distance from vessels along the whole trajectory, optimized for gray matter contact and adjusted to avoid conflicts with other electrode trajectories. The electrode trajectories then are presented for review by the using the probe’s-eye function linked to the orthogonal planes. Then, the resulting electrode trajectories are iterated by using either the “next entry” or
Computer-assisted determination of electrode-placement workflow. A: Using the EpiNav strategy, module ROIs are segmented automatically from the parcellation image. In this example, the cortex (white) is semitransparent to enable visualization of the underlying middle temporal gyrus (yellow), amygdala (blue), and hippocampus (red). B: Entry and target points for the electrodes within the strategy are generated automatically based on the safety metrics defined by the user. Electrodes are indicated in the right amygdala (yellow trajectory), right anterior hippocampus (green trajectory), and right posterior mesial orbitofrontal (blue trajectory). C: A surface risk heat map on the scalp was generated for the mesial orbitofrontal electrode as an example to show the safety of potential trajectory entry points. D: Orthogonal and 3D views showing the target risk heat map was generated for the mesial orbitofrontal electrode as an example to show safe trajectory target points in the orthogonal planes. Note that only 3 electrodes are shown for clarity. A probe’s-eye view (not shown) can then be linked to the orthogonal planes for further assessment of the electrode trajectories. Figure is available in color online only.
Risk Metric Calculation
EpiNav provides a graphic of the minimum distance from vasculature along the length of the electrode and a quantitative representation of the following safety metrics for both manually and CAP-determined electrode-placement plans, which were used for comparison: 1) electrode length, 2) drilling angle, 3) risk, 4) gray/white matter–sampling ratio, and 5) the minimum distance from vessels.
External Validation
Five independent external raters who were neurosurgeons with expertise in performing electrode implantations for SEEG performed the external validation. The external raters had a range of experience with different implantation techniques, including frame-based (J.M.), frameless (D.N.), iSYS1 (S.W. and C.D.), and Neuromate (M.T.) robotic implantation methods. A prospective power calculation based on a pilot study in which 14 electrodes from 2 patients were rated by a surgeon (M.T.) revealed that 24 electrodes were required to detect an absolute difference in risk of 0.2 assuming an SD of 0.3 and a power of 0.90 to achieve a 2-tailed significance level of p = 0.05. To account for a potential clustering effect, a total of 13 patients were recruited. All raters appraised the same 2 pairs of plans (n = 32 electrodes) to assess interrater variability and another 3 or 4 sets of paired plans (n = 34–41 electrodes) independently. All raters were blinded to the electrode-trajectory-generation method and were asked to provide ratings of the entry, trajectory, and target feasibility for paired manually and CAP-determined electrodes. Raters were asked to rate the feasibility of each trajectory based on their current implantation practice. Given that the sampling region suitability had been approved by the MDT based on the noninvasive presurgical evaluation, the raters were asked to comment only on the surgical feasibility of electrode implantation.
Statistical Evaluation
Risk metrics for manually and CAP-determined electrode placement were confirmed to have a normal distribution through the Shapiro-Wilks test (p > 0.05). A paired Student t-test was used for manually and CAP-determined electrode-placement plan comparisons. Clustering of electrodes within patients was assessed by using a patient-specific random-effects model (model 1) and the possible difference between surgeons by using a fixed-effect model (model 2). A generalized likelihood ratio test was performed to compare models 1 and 2, which resulted in a p value of 0.151, indicating that insufficient evidence was found to suggest a significant difference between surgeons with regard to feasibility ratings. Feasibility ratings of electrode-placement plans generated from the manual and CAP methods were compared using the McNemar test, and odds ratios were calculated.
Results
Thirteen consecutive patients who underwent implantation of 116 electrodes for SEEG were included in the study. Manually determined plans were not provided for 12 electrodes out of concern for reaching specified targets safely; however, trajectories for these electrodes were generated with CAP. As such, paired results for the safety metric comparison were available for 104 electrodes (Fig. 2 and Table 2).
https://thejns.org/view/journals/j-neurosu...0.JNS171826.xml
