P31 Optimising trajectories in computer assisted planning for cranial laser interstitial thermal therapy: a machine learning approach

Abstract

ObjectivesOptimal trajectory planning for cranial laser interstitial thermal therapy (cLITT) in drug resistant focal mesial temporal lobe epilepsy (MTLE).DesignA composite ablation score of ablated AHC minus ablated PHG volumes were calculated and normalised. Random forest and linear regression were implemented to predict composite ablation scores and determine the optimal entry and target point combinations to maximize this.SubjectsTen patients with hippocampal sclerosis were included.MethodsComputer Assisted Planning (CAP) cLITT trajectories were generated using entry regions that include the inferior occipital gyri (IOG), middle occipital gyri (MOG), inferior temporal gyri (ITG) and middle temporal gyri (MTG). Target points were varied by sequential erosions and transformations of the centroid of the amygdala. In total 760 trajectory combinations were generated per patient and ablation volumes were calculated based on a conservative 15 mm maximum ablation diameter.ResultsLinear regression was superior to random forest predictions. Linear regression indicated that maximal composite ablation scores were associated with entry points that clustered around the junction of the IOG, MOG and MTG. The optimal target point was a translation of the centroid of the amygdala anteriorly and medially.ConclusionsMachine learning techniques accurately predict composite ablation scores with linear regression outperforming the random forest approach. Optimal CAP entry points for cLITT maximize ablation of the AHC and spare the PHG.