From the Departments of Neurology (Z.H., X.Z., J.L.).
Research Centre for Medical AI (D.J., J.Y., D.L.).
AJNR Am J Neuroradiol. 2023 Jul;44(7):853-860. doi: 10.3174/ajnr.A7911. Epub 2023 Jun 22.
Highly predictive markers of drug treatment outcomes of tuberous sclerosis complex-related epilepsy are a key unmet clinical need. The objective of this study was to identify meaningful clinical and radiomic predictors of outcomes of epilepsy drug treatment in patients with tuberous sclerosis complex.
A total of 105 children with tuberous sclerosis complex-related epilepsy were enrolled in this retrospective study. The pretreatment baseline predictors that were used to predict drug treatment outcomes included patient demographic and clinical information, gene data, electroencephalogram data, and radiomic features that were extracted from pretreatment MR imaging scans. The Spearman correlation coefficient and least absolute shrinkage and selection operator were calculated to select the most relevant features for the drug treatment outcome to build a comprehensive model with radiomic and clinical features for clinical application.
Four MR imaging-based radiomic features and 5 key clinical features were selected to predict the drug treatment outcome. Good discriminative performances were achieved in testing cohorts (area under the curve = 0.85, accuracy = 80.0%, sensitivity = 0.75, and specificity = 0.83) for the epilepsy drug treatment outcome. The model of radiomic and clinical features resulted in favorable calibration curves in all cohorts.
Our results suggested that the radiomic and clinical features model may predict the epilepsy drug treatment outcome. Age of onset, infantile spasms, antiseizure medication numbers, epileptiform discharge in left parieto-occipital area of electroencephalography, and gene mutation type are the key clinical factors to predict the epilepsy drug treatment outcome. The texture and first-order statistic features are the most valuable radiomic features for predicting drug treatment outcomes.
对结节性硬化症相关癫痫药物治疗结果具有高度预测性的标志物是一项重要的临床需求。本研究旨在确定有意义的临床和放射组学预测因子，以预测结节性硬化症患者癫痫药物治疗的结果。
本回顾性研究共纳入 105 例结节性硬化症相关癫痫患儿。用于预测药物治疗结果的预处理基线预测因子包括患者的人口统计学和临床信息、基因数据、脑电图数据以及预处理磁共振成像扫描中提取的放射组学特征。计算 Spearman 相关系数和最小绝对收缩和选择算子，以选择与药物治疗结果最相关的特征，构建一个具有放射组学和临床特征的综合模型，用于临床应用。
选择了 4 个基于磁共振成像的放射组学特征和 5 个关键临床特征来预测药物治疗结果。在测试队列中取得了良好的区分性能（曲线下面积=0.85，准确率=80.0%，敏感性=0.75，特异性=0.83），用于预测癫痫药物治疗结果。放射组学和临床特征模型在所有队列中均产生了良好的校准曲线。
我们的研究结果表明，放射组学和临床特征模型可能预测癫痫药物治疗结果。发病年龄、婴儿痉挛症、抗癫痫药物数量、脑电图左顶枕区癫痫样放电和基因突变类型是预测癫痫药物治疗结果的关键临床因素。纹理和一阶统计特征是预测药物治疗结果最有价值的放射组学特征。
