Departments of1Diagnostic Imaging and.
2Institute of Medical Science, University of Toronto, Ontario, Canada.
J Neurosurg Pediatr. 2023 Oct 6;32(6):739-749. doi: 10.3171/2023.8.PEDS23240. Print 2023 Oct 1.
MR-guided laser interstitial thermal therapy (MRgLITT) is associated with lower seizure-free outcome but better safety profile compared to open surgery. However, the predictors of seizure freedom following MRgLITT remain uncertain. This study aimed to use machine learning to predict seizure-free outcome following MRgLITT and to identify important predictors of seizure freedom in children with drug-resistant epilepsy.
This multicenter study included children treated with MRgLITT for drug-resistant epilepsy at 13 epilepsy centers. The authors used clinical data, diagnostic investigations, and ablation features to predict seizure-free outcome at 1 year post-MRgLITT. Patients from 12 centers formed the training cohort, and patients in the remaining center formed the testing cohort. Five machine learning algorithms were developed on the training data by using 10-fold cross-validation, and model performance was measured on the testing cohort. The models were developed and tested on the complete feature set. Subsequently, 3 feature selection methods were used to identify important predictors. The authors then assessed performance of the parsimonious models based on these important variables.
This study included 268 patients who underwent MRgLITT, of whom 44.4% had achieved seizure freedom at 1 year post-MRgLITT. A gradient-boosting machine algorithm using the complete feature set yielded the highest area under the curve (AUC) on the testing set (AUC 0.67 [95% CI 0.50-0.82], sensitivity 0.71 [95% CI 0.47-0.88], and specificity 0.66 [95% CI 0.50-0.81]). Logistic regression, random forest, support vector machine, and neural network yielded lower AUCs (0.58-0.63) compared to the gradient-boosting machine but the findings were not statistically significant (all p > 0.05). The 3 feature selection methods identified video-EEG concordance, lesion size, preoperative seizure frequency, and number of antiseizure medications as good prognostic features for predicting seizure freedom. The parsimonious models based on important features identified by univariate feature selection slightly improved model performance compared to the complete feature set.
Understanding the predictors of seizure freedom after MRgLITT will assist with prognostication.
与开颅手术相比，磁共振引导激光间质热疗（MRgLITT）术后癫痫无发作的结果较低，但安全性更好。然而，MRgLITT 术后癫痫无发作的预测因素仍不确定。本研究旨在使用机器学习预测 MRgLITT 术后癫痫无发作的结果，并确定耐药性癫痫患儿癫痫无发作的重要预测因素。
本多中心研究纳入了在 13 个癫痫中心接受 MRgLITT 治疗的耐药性癫痫患儿。作者使用临床数据、诊断性检查和消融特征来预测 MRgLITT 术后 1 年的癫痫无发作结果。来自 12 个中心的患者组成训练队列，而来自其余 1 个中心的患者组成测试队列。使用 10 倍交叉验证在训练数据上开发了 5 种机器学习算法，并在测试队列上测量模型性能。在完整的特征集中开发和测试了这些模型。随后，使用 3 种特征选择方法来确定重要的预测因素。然后，作者根据这些重要变量评估简化模型的性能。
本研究纳入了 268 例接受 MRgLITT 的患者，其中 44.4%的患者在 MRgLITT 术后 1 年时癫痫无发作。在测试集中，使用完整特征集的梯度提升机算法获得了最高的曲线下面积（AUC）（AUC 0.67 [95%CI 0.50-0.82]，敏感性 0.71 [95%CI 0.47-0.88]，特异性 0.66 [95%CI 0.50-0.81]）。逻辑回归、随机森林、支持向量机和神经网络的 AUC 较低（0.58-0.63），但与梯度提升机相比，差异无统计学意义（均 P>0.05）。3 种特征选择方法确定视频脑电图一致性、病变大小、术前发作频率和抗癫痫药物数量为预测癫痫无发作的良好预后特征。基于单变量特征选择确定的重要特征的简化模型与完整特征集相比，模型性能略有提高。
了解 MRgLITT 后癫痫无发作的预测因素将有助于预测结果。
