Vakharia Vejay N, Sparks Rachel E, Granados Alejandro, Miserocchi Anna, McEvoy Andrew W, Ourselin Sebastien, Duncan John S
Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom.
National Hospital for Neurology and Neurosurgery, London, United Kingdom.
Front Neurol. 2020 Jul 17;11:706. doi: 10.3389/fneur.2020.00706. eCollection 2020.
Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using "spatial priors" derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.
立体定向脑电图（SEEG）是一种将多个电极通过立体定向技术植入大脑不同区域的操作，用于评估药物难治性局灶性癫痫患者的致痫区。计算机辅助规划（CAP）可在手动规划所需时间的一小部分内提高风险评分、灰质采样、与颅骨的正交钻孔角度以及脑内长度。由于规划实践的差异，此类算法在不同机构之间可能无法通用。我们使用先前植入产生的“空间先验”对临床可行轨迹进行前瞻性验证，并实施机器学习分类器以适应不断变化的规划实践。32例患者在2年期间连续接受了利用计算机辅助规划的SEEG植入。前12例患者（108个电极）植入的电极用作训练集，从中生成入口点和靶点的空间先验。然后使用空间先验对另外20例患者（210个电极）的测试集进行前瞻性CAP。实施K近邻（K-NN）机器学习分类器作为自适应学习方法，以动态修改空间先验。所有318个前瞻性计算机辅助规划电极均顺利植入，无并发症。从训练集得出的空间先验在79%的测试集中生成了临床可行轨迹。其余21%需要空间先验之外的入口点或靶点。K-NN分类器能够动态模拟空间先验的实时变化，以适应不断变化的规划要求。我们为常见的SEEG轨迹提供空间先验，前瞻性地整合了先前SEEG植入的临床可行轨迹规划实践。这使得机构的SEEG经验能够被纳入并用于指导未来的植入。随着进一步植入的进行，K-NN分类器的部署可通过实时动态修改空间先验来提高算法的通用性。
