Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

项目摘要/摘要 在美国，中风是第五大死因。血管内卒中治疗(EST)有 革命性地管理大血管闭塞(LVO)急性缺血性中风(AIS)，这说明 对于中风中不成比例的残疾。虽然这种疗法已经被证明显著改善了 在多个临床试验中的临床结果，这些研究几乎都需要筛查晚期患者 神经成像，如CT灌注(CTP)，这是一种大多数社区医院不提供的方式。AS 因此，迫切需要一种能够识别已经利用基础设施的EST候选者的工具 存在于社区医院。我们设想了一种基于软件的服务来自动进行神经成像 CT血管成像(CTA)对EST的评价我们开发并测试了一种新型深层神经的原型。 称为DeepSymNet的网络体系结构。我们的初步数据表明，只有使用CTA，我们才能 确定(1)有无大血管闭塞(2)缺血核心的范围和(3) “危险”组织的体积(半影)高于或低于临床试验中使用的阈值，当 与使用CTP同时获得的结果进行比较。 我们将带着三个目标追求我们的项目目标： -目标1-为急性脑缺血的神经成像研究建立最大的多机构数据库之一 卒中。我们将获得一个多中心的数据集，包括来自15家医院的成像和临床数据 德克萨斯州和加利福尼亚州，来自一系列扫描仪、成像采集协议和医院类型(即大型 学术和更小的社区)。 -目标2-开发可解释的深度学习模型，以确定EST的资格。我们会有条不紊地 测试一组基于以下各项的模型体系结构、数据扩展策略、损失函数和预处理步骤 在DeepSymNet上。我们将针对脑梗塞核心和半影区的不同定义来训练和测试算法 基于CTP结果的成交量。这种方法将允许模型适应不断变化的定义 EST资格。 -目标3-评估基于DeepSymNet的模型在大型多中心独立模型上的外部有效性 数据集。为了实现这一目标，我们将在患者成像和数据上部署我们的DeepSymNet软件 多家医院，这些医院在创建软件时没有使用。我们还将验证我们的方法 单独使用CTA通过验证CTA来源的梗塞核心的盲读数来确定缺血核心 专家阅读器对照同时获取的CTP结果执行的图像。 完成这些目标将产生持续的、变革性的影响，支持创建和 验证决策支持工具可在绝大多数社区的患者床边轻松翻译 全国各地的医院。通过这样做，我们希望扩大获得高质量EST筛查的机会，以 数以千计的AIS患者。

Project Summary/Abstract Stroke is the 5th leading cause of death in the United States. Endovascular stroke therapy (EST) has revolutionized the management of large vessel occlusion (LVO) acute ischemic stroke (AIS), which accounts for a disproportionate amount of disability in stroke. While this therapy has been shown to significantly improve clinical outcomes in multiple clinical trials, these studies nearly all required screening patients with advanced NeuroImaging such as CT Perfusion (CTP), a modality not available to the majority of community hospitals. As such, there is a pressing need to for a tool able to identify EST candidates leveraging the infrastructure already existing in community hospitals. We envision a software-based service to automate the NeuroImaging evaluation for EST using CT angiography (CTA). We developed and tested a prototype of a novel deep neural network architecture called DeepSymNet. Our preliminary data indicate that uniquely using CTAs we can determine (1) the presence or absence of a large vessel occlusion (2) if the extent of ischemic core and (3) volume of tissue “at risk” (penumbra) is above or below the thresholds used in the clinical trials, when compared to concurrently obtained results using CTP. We will pursue our project goal with three aims: - Aim 1 - Establish one of the largest multi-institution dataset for neuro-imaging research in acute ischemic stroke. We will acquire a multi-center dataset including imaging and clinical data from 15 hospitals across Texas and California, from a range of scanners, imaging acquisition protocols, and hospital types (i.e. large academic and smaller community). - Aim 2 - Develop interpretable deep learning models to determine the eligibility for EST. We will methodically test a set of model architectures, data augmentation strategies, loss functions and pre-processing steps based on DeepSymNet. We will train and test the algorithm against various definitions of infarct core and penumbral volume based on CTP results. This approach will allow for models adaptable to the everchanging definition of EST eligibility. – Aim 3 - Evaluate the external validity of DeepSymNet-based models on a large multi-center independent dataset. To accomplish this aim, we will deploy our DeepSymNet software on patient imaging and data from multiple hospitals, which were not used in the creation of the software. We will also validate our approach of using CTA alone to determine ischemic core by validating blinded reads of infarct core from CTA source images performed by expert readers against concurrently acquired CTP results. Completion of these aims will have a sustained, transformative impact by supporting the creation and validation of decision support tools readily translatable to the patient bedside in the vast majority of community hospitals across the country. In doing so, we hope to expand the access to high-quality EST screening to thousands of additional AIS patients.