Brain MRI to pre-symptomatically predict seizure onset for Sturge-Weber Syndrome

摘要 Sturge-Weber综合征（SWS）是一种罕见的神经系统疾病，其最大的问题是神经认知 按学龄（6-10岁）分列的残疾情况。为了改善神经认知的结果，肯尼迪克里格研究所（博士。 Comi，该R21的共同主要研究者）和波士顿儿童医院（平托博士，该R21的共同主要研究者）一直是领先的或 各种临床试验的关键部位，以测试新治疗（NCT 02332655（2014-19）; NCT 0304980（2017-19）; NCT 04447846（2019-21）），或开发可以选择风险患者的神经影像学生物标志物 （NCT 01345305（2010-2012）; NCT 01425944（2010-2020）; NCT 04717427（2021-2024））。然而，所有这些试验 重点放在症状后阶段-癫痫症状发生后。我们最近的证据表明 症状前治疗-在癫痫症状发生前治疗患者，理想情况下在2年前， 年龄-可能会延迟或避免癫痫症状。这很重要，因为那些没有癫痫症状的人， 10-25%的SWS患者在学龄前通常享有良好的神经认知结果。出于 为此，多学科专家在2018年，2019年和2021年聚集并达成共识，呼吁立即 症状前治疗的研究。为了及时响应这一呼吁，KKI和BCH，两个最大的 对SWS进行术前治疗的国家中心正在计划进行一项试验， 抗癫痫药物左乙拉西坦在两个治疗组中的作用（左乙拉西坦联合与不联合低剂量 阿司匹林）进行对症治疗。然而，这项计划中的试验的瓶颈问题是缺乏一个 生物标志物，用于准确和预先识别有癫痫发作风险的SWS患者 2岁时出现症状。这些高危患者应该是我们计划中的理想候选人。 审判这个R21旨在解决这个瓶颈生物标志物问题。我们计划回溯性地建造 来自KKI和BCH临床数据的多中心症状前数据库（目的1）。我们将全面评估两个 临床和脑MRI生物标志物，以在术前识别风险患者（目标2）。中央 假设人工智能（AI）算法从临床和大脑中提取的复杂特征 MRI可以作为一种生物标志物，在诊断前识别有癫痫发作风险的SWS患者 2岁时出现症状。这是第一个由人工智能驱动的、大数据集驱动的、针对症状前的严格研究 这种罕见疾病的临床和MRI生物标志物。这种生物标志物将立即用于我们计划的 评价左乙拉西坦联合或不联合低剂量阿司匹林用于症状前治疗的临床试验。

Abstract Sturge-Weber syndrome (SWS) is a rare neurological disease, and its biggest concern is neurocognitive impairments by school age (6-10 years). To improve neurocognitive outcomes, Kenndy Krieger Institute (Dr. Comi, co-PI of this R21) and Boston Children’s Hospital (Dr. Pinto, co-PI of this R21) have been the leading or key sites in various clinical trials, to test new treatment (NCT02332655 (2014-19); NCT0304980 (2017-19); NCT04447846 (2019-21)), or to develop neuroimaging biomarkers that can select at-risk patients (NCT01345305 (2010-2012); NCT01425944 (2010-2020); NCT04717427 (2021-2024)). However, all these trials focus on the post-symptomatic phase – after seizure symptoms have occurred. Our recent evidence suggested that pre-symptomatic treatment – treating patients before seizure symptoms occur, ideally before 2 years of age – may delay or avoid seizure symptoms. This is important, because those without seizure symptoms by 2 years of age (10-25% of SWS patients) often enjoy good neurocognitive outcomes by school age. Motivated by this, multidisciplinary experts gathered and reached a consensus in 2018, 2019, and 2021, calling for immediate investigations of pre-symptomatic treatments. In a timely response to this call, KKI and BCH, the two largest national centers that treat SWS pre-symptomatically, are planning for a trial to comprehensively evaluate the effect of anti-epilepsy drugs Levetiracetam, in two treatment arms (Levetiracetam with versus without low-dose aspirin) for pre-symptomatic treatment. The bottleneck issue for this planned trial, though, is the lack of a biomarker to accurately and pre-symptomatically identify SWS patients who are at risk to develop seizure symptoms by 2 years of age. Those at-risk patients should be ideal candidates to be included in our planned trial. This R21 aims to address this bottleneck biomarker problem. We plan to retrospectively build the largest multi-site presymptomatic database from clinical data in KKI and BCH (Aim 1). We will thoroughly evaluate two clinical and brain MRI biomarkers to identify at-risk patients pre-symptomatically (Aim 2). The central hypothesis is that sophisticated features that artificial intelligence (AI) algorithms extract from clinical and brain MRI could serve as a biomarker to pre-symptomatically identify SWS patients at risk of developing seizure symptoms by 2 years of age. This is the first AI-powered, large-dataset-driven rigorous study for presymptomatic clinical and MRI biomarkers for this rare disease. Such a biomarker will be immediately used in our planned clinical trials to evaluate Levetiracetam with or without low-dose aspirin for pre-symptomatic treatment.