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首页> 外文期刊>Journal of Neurology, Neurosurgery and Psychiatry >P31?Optimising trajectories in computer assisted planning for cranial laser interstitial thermal therapy: a machine learning approach
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P31?Optimising trajectories in computer assisted planning for cranial laser interstitial thermal therapy: a machine learning approach

机译：P31？优化计算机辅助规划颅激激术热疗的计算机辅助规划轨迹：机器学习方法

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摘要

Optimal trajectory planning for cranial laser interstitial thermal therapy (cLITT) in drug resistant focal mesial temporal lobe epilepsy (MTLE).A 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.Ten patients with hippocampal sclerosis were included.Computer 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.Linear 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.Machine 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.
机译：抑制局灶性颞叶癫痫（咒语）中颅激激热疗（CLITT）的最佳轨迹规划。计算烧蚀AHC减去消融烧蚀量的复合消融得分。实施随机森林和线性回归以预测复合消融评分，并确定最大化的最佳进入和目标点组合，以最大化。包括海马硬化的患者..使用包括劣势的进入区域产生电脑辅助规划（CAP）Clitt轨迹枕骨Gyri（IOG），中枕吉尔里（沼泽），较差的颞甘氨酸（ITG）和中间颞甘氨酸（MTG）。序列糜烂和杏仁菌的质心的转化变化了目标点。总共产生760个轨迹组合，并且基于保守的15Ωmm的最大烧蚀直径计算消融体积。线性回归优于随机森林预测。线性回归表明，最大复合消融分数与聚集在IOG，MOG和MTG的结聚集的入口点相关联。最佳目标点是前后和内侧的Amygdala的质心的翻译准确地预测了具有线性回归优于随机森林方法的线性回归的复合消融评分。 CLITT的最佳帽入口点最大化AHC的消融并备用PHG。

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    K Li; VN Vakharia; R Sparks; LGS Fran?a; A McEvoy; A Miserocchi; S Ourselin; J Duncan;

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    1Department of Clinical and Experimental Epilepsy University College London;

    1Department of Clinical and Experimental Epilepsy University College London;

    2Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London;

    1Department of Clinical and Experimental Epilepsy University College London;

    3National Hospital for Neurology and Neurosurgery London UK;

    3National Hospital for Neurology and Neurosurgery London UK;

    4School of Biomedical Engineering and Imaging Sciences St Thomas’ Hospital King’s College London;

    1Department of Clinical and Experimental Epilepsy University College London;

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  • 正文语种 eng
  • 中图分类 神经病学;
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