Clinical Medical College, Jining Medical University, Jining, China.
Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China.
J Magn Reson Imaging. 2024 Dec;60(6):2309-2331. doi: 10.1002/jmri.29157. Epub 2023 Nov 28.
Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
在全球约 2000 万耐药性癫痫(DRE)患者中,绝大多数可以从手术中获益,以最大程度地减少癫痫发作和神经损伤。精确的术前致痫区(EZ)定位和病变的完全切除可以影响术后预后。然而,EZ 的精确定位较为困难,并且 DRE 引起的大脑结构和功能改变因病因而异。神经影像学已成为识别大脑中致痫结构和功能变化的一种方法,磁共振成像(MRI)和正电子发射断层扫描(PET)已成为许多癫痫治疗中心术前评估 DRE 的常规无创成像工具。多模态神经影像学在检测 EZ 方面具有独特的优势,特别是在提高 MRI 或 PET 阴性发现的患者的检出率方面。这种方法可以对不同病因引起的 DRE 患者的脑影像学特征进行分类,成为临床和病理发现之间的桥梁,并为个体化临床治疗计划提供依据。除了整合多模态成像方式和开发特殊扫描序列和图像后处理技术以早期和精确地定位 EZ 外,还应用深度学习来提取图像特征和基于深度学习的人工智能,逐渐提高了诊断效率和准确性。这些改进可以为精确勾勒 EZ 范围并指示 EZ 与功能脑区之间的关系提供临床帮助,从而实现标准化和精确的手术,并确保良好的预后。然而,大多数现有研究受到样本量小或基于假设的研究设计等因素的限制。因此,我们认为 DRE 中神经影像学和后处理技术的应用需要进一步发展,并且在临床实践中迫切需要更高效和准确的成像技术。证据水平:5 技术功效:阶段 2。