Prediction of seizure lateralization and postoperative outcome through the use of deep learning applied to multi-site MRI/DTI data: An ENIGMA-Epilepsy study

通过将深度学习应用于多部位 MRI/DTI 数据来预测癫痫偏侧化和术后结果：ENIGMA-癫痫研究

• 批准号: 9751025
• 负责人: Leonardo F Bonilha
• 金额: $ 44.43万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9751025
• 关键词: Address; Affect; Age; Age of Onset; Algorithms; Antiepileptic Agents; Benchmarking; Capital; Characteristics; Classification; Clinical; Clinical Data; Complex; Country; Coupled; Data; Data Collection; Data Set; Databases; Development; Diagnostic; Diagnostic Procedure; Diffusion Magnetic Resonance Imaging; Disease; Electroencephalography; Epilepsy; Ethnic Origin; Evaluation; Geography; Gold; Grant; Image; Individual; Infrastructure; Institution; Lead; Left; Lesion; Lifting; Machine Learning; Magnetic Resonance Imaging; Methods; Modeling; Multimodal Imaging; National Institute of Neurological Disorders and Stroke; Network-based; Neurologic; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patients; Pattern; Pharmacotherapy; Play; Population; Postoperative Period; Prediction of Response to Therapy; Probability; Reproducibility; Reproducibility of Results; Research; Resources; Role; Sample Size; Sampling; Scanning; Seizures; Site; Structure; Syndrome; Talents; Techniques; Temporal Lobe Epilepsy; Testing; Thinness; United States National Institutes of Health; Validation; base; biomarker identification; brain abnormalities; classification algorithm; cohort; computing resources; connectome; cost; cost effective; deep learning; design; gray matter; hands-on learning; imaging study; improved; innovation; interest; machine learning algorithm; nervous system disorder; neural network; neuroimaging; novel; novel strategies; personalized approach; sex; standard of care; surgery outcome; white matter

ABSTRACT Epilepsy is a devastating neurological illness that affects 65 million people worldwide. Approximately one-third of patients affected do not respond to antiepileptic drug therapy and require a thorough diagnostic work-up. Structural neuroimaging plays a pivotal role in the diagnostic evaluation of patients with focal epilepsy, identifying visible lesions in many patients that often coincide with the seizure focus. However, 20- 40% of patients have normal-appearing MRIs and this number appears to be growing. As a result, there is increased interest in identifying subtle gray and white matter network changes on non-invasive, quantitative MRI, including structural MRI (sMRI) and diffusion tensor imaging (DTI), that can help to delineate the epileptogenic network. Unfortunately, methods for selecting optimal features from sMRI/DTI data in patients with epilepsy that can address these clinical challenges have not been developed. There are at least two major barriers that have limited progress in this field. First, sample sizes have been insufficient to develop reliable classification algorithms in patients with focal epilepsy that lead to reproducible findings. The high cost of data collection - few studies scan more than 50-60 patients - has led to underpowered studies whose findings often fail to replicate and cannot adequately model confounds. Second, high computational demands have previously limited the feasibility of using sophisticated, feature-selection (i.e., Machine Learning; ML) algorithms in clinical settings. A new, large-scale data initiative (i.e., ENIGMA-epilepsy) acquired from 24 sites world-wide is now lifting these barriers and allowing for the development and validation of innovative data-driven approaches aimed at optimizing the use of MRI data in the evaluation of epilepsy. In this grant, we will leverage data collected through ENIGMA-Epilepsy—a new, cost-effective, innovative global approach that unblocks the power logjam by merging resources, data, capital infrastructure and talents of leading epilepsy centers from 14 countries across the world (2,149 patient and 1,727 healthy control MRI/DTI datasets). We will also leverage new developments in ML (i.e., deep learning) and network-based modeling (i.e., connectome- based approaches) and test whether these novel approaches improve upon classification accuracy relative to simpler, user-driven models. Our primary aim will be to test the ability of our deep learning approach (i.e., dense neural networks) to lateralize the seizure focus. In an exploratory aim, we will test the ability of our model to predict post-operative seizure outcomes. ENIGMA's harmonized approach will allow us to test our approach in over 24 datasets, diverse in age, ethnicity, age of onset, epilepsy duration, and surgical outcomes. This R-21 application addresses NIH's call for more reproducible studies by introducing a highly- powered design, and is directly aligned with NINDS's 2014 Epilepsy Benchmarks, which encourage the identification of biomarkers for assessing or predicting treatment response in patients with epilepsy.

抽象的 癫痫是一种毁灭性的神经系统疾病，影响着全世界大约 6500 万人。 三分之一受影响的患者对抗癫痫药物治疗没有反应，需要彻底诊断 结构神经影像学检查在局灶性患者的诊断评估中发挥着关键作用。 癫痫，识别许多患者的可见病变，这些病变通常与癫痫病灶一致，但是，20- 40% 的患者 MRI 表现正常，而且这个数字似乎还在增长。 对非侵入性、定量识别微妙的灰质和白质网络变化的兴趣增加 MRI，包括结构 MRI (sMRI) 和扩散张量成像 (DTI)，可以帮助描绘 不幸的是，从患者的 sMRI/DTI 数据中选择最佳特征的方法。 至少有两种药物可以解决这些临床挑战。 首先，样本量不足以开发。 局灶性癫痫患者的可靠分类算法可得出可重复的结果。 数据收集成本高昂 - 很少有研究扫描超过 50-60 名患者 - 导致研究动力不足 其研究结果往往无法复制，也无法充分模拟混杂因素。 以前的需求限制了使用复杂的特征选择（即机器 临床环境中的学习；ML）算法。 从全球 24 个站点获取的一项新的大规模数据计划（即 ENIGMA-epilepsy）现已投入使用 消除这些障碍并允许开发和验证创新的数据驱动方法 旨在优化 MRI 数据在癫痫评估中的使用。在这笔资助中，我们将利用数据。 通过 ENIGMA-Epilepsy 收集 - 一种新的、具有成本效益的、创新的全球方法，可以解除障碍 通过合并领先癫痫中心的资源、数据、资本基础设施和人才来解决电力僵局 来自全球 14 个国家（2,149 名患者和 1,727 名健康对照 MRI/DTI 数据集）。 还利用机器学习（即深度学习）和基于网络的建模（即连接组）的新发展 基于方法）并测试这些新方法是否相对于分类精度有所提高 我们的主要目标是测试我们的深度学习方法的能力（即， 密集的神经网络）来偏向癫痫焦点在探索性目标中，我们将测试我们的能力。 ENIGMA 的统一方法将使我们能够测试我们的模型来预测术后癫痫发作结果。 该方法在超过 24 个数据集中进行，这些数据集的年龄、种族、发病年龄、癫痫持续时间和手术结果各不相同。 该 R-21 应用程序通过引入高度重复性的方法，满足了 NIH 对更具可重复性研究的呼吁 动力设计，并直接与 NINDS 的 2014 年癫痫基准保持一致，该基准鼓励 鉴定用于评估或预测癫痫患者治疗反应的生物标志物。