Pattern Classification Using Magnetic Resonance Imaging in Traumatic Brain Injury

项目摘要 在美国，轻度创伤性脑损伤（MTBI）每年影响约150万人，其中15%至30% 受伤后长期残疾的患者。我们仍处于理解这一点的早期阶段 疾病和研究疾病和开发适当治疗的最大障碍之一是 诊断和预后预测困难。一般来说，MTBI的诊断依赖于使用格拉斯哥 昏迷量表（GCS），一种15分的睁眼、运动和语言反应的粗略测量。国家 2014年神经疾病和卒中研究所（NINDS）研讨会指出，使用GCS评分作为 TBI的单一分类器是不够的，并提出神经影像学在TBI的治疗中发挥更大的作用。 制定诊断和预后预测的客观标准。我们有具体的经验， 研究新的MRI技术，在评估MTBI患者方面显示出很大的希望。当前的目标 该提案旨在将这些新的MRI技术应用于临床。我们建议联合收割机将来自 客观的MR影像学特征结合临床信息，学习最能区分患者的模式 并使用机器学习预测长期结果。我们将使用单独的 受试者队列。这样的工具将是一个非常强大的临床工具，以确定风险患者的早期治疗。 干预此外，这项研究将确定最临床相关的MR指标，从而指出 新的治疗途径。

PROJECT SUMMARY Mild traumatic brain injury (MTBI) affects ~1.5 million persons annually in the United States with fifteen to 30% of patients suffering long-term disability after injury. We remain in the early phase of understanding this disease and one of the greatest barriers to studying the disease and developing appropriate therapy is the difficulty in diagnosis and outcome prediction. Generally, the diagnosis of MTBI relies on using the Glasgow Coma Scale (GCS), a 15-point gross measurement of eye-opening, motor and verbal response. The National Institute for Neurological Disorders and Stroke (NINDS) workshop in 2014 indicated that use of GCS score as a single classifier for TBI is insufficient and proposed that neuroimaging play a larger role towards the development of objective criteria for diagnosis and outcome prediction. We have specific experience in studying novel MRI techniques that show much promise in evaluating MTBI patients. The goal of the current proposal is to bring these novel MRI techniques to clinical use. We propose to combine information from objective MR imaging features with clinical information to learn the patterns that can best distinguish patients from controls and predict long-term outcome using machine learning. We will validate our tool using a separate subject cohort. Such a tool would be an extremely powerful clinical tool to identify at-risk patients for early intervention. Additionally, this research will identify the most clinically relevant MR metrics, thereby pointing the way to novel therapeutic pathways.