Pattern Classification Using Magnetic Resonance Imaging in Traumatic Brain Injury

项目总结 在美国，轻度创伤性脑损伤(MTBI)每年影响约150万人，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 疾病和研究这种疾病和开发适当治疗方法的最大障碍之一是 诊断和结果预测困难。一般来说，MTBI的诊断依赖于使用格拉斯哥 昏迷评分(GCS)，是对睁眼、运动和言语反应的15分粗略测量。《国家报》 神经疾病和中风研究所(NINDS)2014年的研讨会表明，使用GCS评分作为 脑外伤的单一分类器是不够的，并提出神经成像在脑外伤中发挥更大的作用。 制定诊断和结果预测的客观标准。我们在以下方面有具体的经验 研究新的MRI技术，这些技术在评估MTBI患者方面显示出很大的前景。当前的目标是 建议将这些新的核磁共振技术应用于临床。我们建议结合来自以下方面的信息 摘要目的结合临床资料分析磁共振成像特征，了解最能区分患者的影像特征。 并使用机器学习来预测长期结果。我们将使用单独的 受试者队列。这样的工具将是一种极其强大的临床工具，可以早期识别高危患者 干预。此外，这项研究将确定最具临床相关性的磁共振指标，从而指出 通向新的治疗途径的途径。

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.