Impact of entry trajectory angle-to-skull on electrode placement accuracy in robot-assisted stereoelectroencephalography.

OBJECTIVE

Despite the widespread use of robotic systems to improve stereotactic accuracy in stereoelectroencephalography (SEEG), targeting errors still occur. Several studies have suggested that the trajectory-to-skull angle is closely associated with targeting error among various influencing factors; however, quantitative analyses remain limited. Therefore, this study aimed to quantitatively evaluate the relationship between trajectory-to-skull angle and implantation accuracy and to establish practical cut-off values for surgical planning.

METHODS

A total of 10 patients (103 electrodes) were included in this study and various clinical variables were analyzed to identify whether trajectory-to-skull angle has a significant relation with accuracy using multivariate linear regression. Then, receiver operating characteristic (ROC) curve analysis with Youden index optimization was applied to determine angle thresholds for achieving ≤ 3 mm accuracy.

RESULTS

Multivariate analysis showed that both axial and coronal trajectory-to-skull angles were significantly associated with target error, with more tangential incidence angles corresponding to larger deviations (p < 0.01). Axial angle-to-skull was particularly associated with anteroposterior (Y-axis) error, whereas coronal angle-to-skull primarily predicted superoinferior (Z-axis) error. ROC curve analysis identified optimal angle thresholds of 11.8° for axial trajectories and 6.3° for coronal trajectories to predict targeting accuracy within 3 mm. When both axial and coronal angles were maintained within these cut-offs, the probability of achieving a target error ≤ 3 mm was 88.2%.

CONCLUSION

Trajectory-to-skull angle is a key geometric determinant of electrode placement accuracy in robot-assisted SEEG. Quantitative angle thresholds identified in this study provide practical guidance for trajectory planning and may help minimize mechanical deviation, improve implantation safety, and enhance the reliability of seizure-network localization.