Qiu Lu, Wang Miaoyan, Liu Surui, Peng Bo, Hua Ying, Wang Jianbiao, Hu Xiaoyue, Qiu Anqi, Dai Yakang, Jiang Haoxiang
Department of Diagnostic Radiology, Affiliated Children's Hospital of Jiangnan University, Wuxi, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Korean J Radiol. 2025 May;26(5):485-497. doi: 10.3348/kjr.2024.0718.
To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE).
We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson's partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation.
In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, < 0.05), and significantly lower DTI-ALPS index (F = 2.0, = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ ≤ 0.32) and nodal efficiency (0.22 ≤ ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ ≤ -0.34) and seizure frequency ( = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%).
Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.
利用多参数磁共振成像（MRI）探讨小儿难治性癫痫（RE）中的类淋巴系统损伤，评估其与白质（WM）异常及临床指标的关系，并初步评估多参数MRI在鉴别RE与药物敏感性癫痫（DSE）方面的性能。
我们回顾性纳入了70例DSE患者（平均年龄9.7±3.5岁；男∶女 = 37∶33）和26例RE患者（9.0±2.9岁；男∶女 = 12∶14）。测量并比较了RE组和DSE组患者沿血管周围间隙的扩散张量成像分析（DTI-ALPS）指数以及分数各向异性（FA）、平均扩散率（MD）和节点效率值。以性别和年龄作为协变量，使用基于纤维束的空间统计学分析FA和MD值的差异，使用线性模型分析节点效率。分析Pearson偏相关性。通过五折交叉验证，使用受试者工作特征（ROC）曲线评估基于MRI的机器学习模型的鉴别性能。
与DSE组的相应值相比，RE组的FA降低而MD升高，这些差异主要累及胼胝体、左右放射冠、上下纵束和丘脑后辐射（无阈值簇增强，<0.05）。RE组还表现出节点效率降低，主要累及边缘系统、默认模式网络和视觉网络（错误发现率，<0.05），且DTI-ALPS指数显著降低（F = 2.0，P = 0.049）。DTI-ALPS指数与FA呈正相关（0.25≤r≤0.32），与节点效率呈正相关（0.22≤r≤0.37），与MD呈负相关（-0.24≤r≤-0.34），与癫痫发作频率呈负相关（r = -0.47）。结合DTI-ALPS、FA、MD和节点效率的机器学习模型实现了0.83的交叉验证ROC曲线面积（敏感性为78.2%；特异性为84.8%）。
与DSE患者相比，小儿RE患者表现出类淋巴功能受损，这与WM异常和癫痫发作频率相关。多参数MRI可能有助于鉴别RE与DSE。
