Radiomics signatures and patient outcomes in intracerebral hemorrhage

脑出血的放射组学特征和患者结果

• 批准号: 10415001
• 负责人: Seyedmehdi Payabvash
• 金额: $ 19.49万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415001
• 关键词: Address; Admission activity; Affect; Age; Atlases; Award; Biologic Characteristic; Biological; Biological Markers; Brain; Brain Injuries; Brain hemorrhage; Brain scan; Caring; Cellularity; Cerebral hemisphere hemorrhage; Cerebrovascular Disorders; Cerebrum; Characteristics; Clinical; Clinical Trials; Computer Vision Systems; Data; Deterioration; Development Plans; Diagnosis; Diagnostic; Edema; Funding; Future; Genomics; Goals; Grant; Growth; Head; Healthcare; Hematoma; Hemoglobin; Hemorrhage; Heterogeneity; High Performance Computing; Hour; Human; Image; Image Analysis; Inflammatory; Infrastructure; Knowledge; Lead; Lesion; Linear Models; Location; London; Machine Learning; Magnetic Resonance Imaging; Mathematics; Medical Imaging; Mentored Patient-Oriented Research Career Development Award; Mentorship; Nature; Necrosis; Neurologic; Neurologic Deficit; Neurologic Symptoms; Neurosciences; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Physicians; Positioning Attribute; Process; Prognostic Factor; Proteomics; Radiology Specialty; Reading; Registries; Research; Research Personnel; Resources; Risk; Risk Factors; Rogaine; Scientist; Services; Severities; Shapes; Signal Transduction; Statistical Data Interpretation; Stroke; Symptoms; Techniques; Texture; Tissues; Training; United States National Institutes of Health; Universities; Visual; Writing; X-Ray Computed Tomography; automated analysis; base; bioimaging; blood-brain barrier disruption; career; career development; clinical risk; college; computerized; cytotoxic; data archive; deep neural network; density; disability; effective therapy; evidence base; feature extraction; feature selection; follow-up; functional independence; image processing; imaging biomarker; improved; innovation; machine learning algorithm; machine learning model; metabolomics; modifiable risk; neuroimaging; neuroimaging marker; new therapeutic target; novel; online repository; outcome prediction; personalized care; personalized medicine; precision medicine; professor; prognostic; prognostication; quantitative imaging; radiomics; research and development; risk stratification; skills; statistics; tool; treatment optimization

PROJECT SUMMARY / ABSTRACT The following K23 proposal is for Dr. Sam Payabvash, a Neuroradiologist and Assistant Professor of Radiology at Yale University. Dr. Payabvash is a physician-scientist with specialized expertise at the intersection of neuroscience, neuroimaging, and computer vision. His career goal is to find new treatment targets and to provide personalized care for patients with cerebrovascular disease. Intracerebral hemorrhage (ICH) is one of the most devastating cerebrovascular diseases with no effective treatment. To date, imaging markers of ICH risk- stratification and outcome prediction have been subjective and descriptive in nature, leaving a large gap for automated assessment of imaging feautres embedded in medical images. Preliminary results by Dr. Payabvash have demonstrated the feasibility of a research plan to apply automated feature extraction pipelines and machine learning algorithms to harness the information in medical images for early risk-stratification and identification of potential treatment targets in ICH. In this proposal, Dr. Payabvash will use detailed clinical and imaging data of 3,991 patients from NIH-funded clinical trials, online archives, and institutional registries at Yale, Tufts, and University College of London. He will apply machine-learning algorithms to identify those imaging features of brain hemorrhage on baseline head CT scan that are related to symptom severity at presentation (aim 1). Then, he will use imaging features of hemorrhage to identify those patients who are at risk for early expansion of hematoma (aim 2a), or surrounding edema (aim 2b). These two “modifiable” indicators of poor outcome are considered potential treatment targets in ICH patients. Finally, he will combine admission clinical information and imaging features to build a risk-stratification tool for long-term outcome prediction (aim 3). Under the expert mentorship of Dr. Kevin Sheth (Chief of Neurocritical Care), Dr. Todd Constable (Director of MRI Research), and Dr. Ronald Coifman (Professor of Mathematics), this K23 award will allow Dr. Payabvash to (1) identify and address the most pressing issues in cerebrovascular disease with innovative neurogaming tools; (2) gain expertise in advanced statistical analysis of brain scans; and (3) expand his knowledge in machine learning and computer vision for assessment of medical images. Dr. Payabvash will receive didactic training in neuroimaging statistical analysis, machine learning, deep neural networks, and computer vision. The proposed research and career development plans draw on the wealth of resources available at Yale, including a Regional Coordinating Center for the NIH StrokeNet, the Center for Research Computing; High Performance Computing services, and cutting-edge image processing and analysis infrastructure. At the conclusion of this award period, Dr. Payabvash will be well-positioned to become an independently-funded investigator conducting high-quality research in advanced neuroimaging techniques and analysis aimed at improving the care of patients with cerebrovascular disease.

项目概要/摘要 以下 K23 提案针对神经放射学家兼放射学助理教授 Sam Payabvash 博士 在耶鲁大学。 Payabvash 博士是一位医学科学家，在以下领域拥有专业知识： 神经科学、神经影像学和计算机视觉。他的职业目标是寻找新的治疗目标并提供 为脑血管病患者提供个性化护理。脑出血（ICH）是最常见的出血性疾病之一 严重的脑血管疾病没有有效的治疗方法。迄今为止，ICH 风险的影像学标志物 分层和结果预测本质上是主观的和描述性的，给 对医学图像中嵌入的成像特征进行自动评估。 Payabvash 博士的初步结果 已经证明了应用自动特征提取管道和机器的研究计划的可行性 学习算法利用医学图像中的信息进行早期风险分层和识别 ICH 的潜在治疗目标。在这项提案中，Payabvash 博士将使用详细的临床和影像数据 3,991 名患者来自 NIH 资助的临床试验、在线档案以及耶鲁大学、塔夫茨大学和 伦敦大学学院。他将应用机器学习算法来识别这些图像特征 基线头部 CT 扫描显示的脑出血与就诊时的症状严重程度相关（目标 1）。然后， 他将利用出血的影像学特征来识别那些有早期扩张风险的患者。 血肿（目标 2a）或周围水肿（目标 2b）。这两个“可修改”的不良结果指标是 被认为是 ICH 患者的潜在治疗目标。最后，他将结合入院临床信息和 成像特征来构建用于长期结果预测的风险分层工具（目标 3）。在专家的指导下 Kevin Sheth 博士（神经重症监护主任）、Todd Constable 博士（MRI 研究总监）的指导 Ronald Coifman 博士（数学教授），该 K23 奖项将使 Payabvash 博士能够 (1) 识别和 通过创新的神经游戏工具解决脑血管疾病中最紧迫的问题； (2)增益 脑部扫描高级统计分析的专业知识； (3) 扩展他在机器学习方面的知识 用于评估医学图像的计算机视觉。 Payabvash 博士将接受神经影像学方面的教学培训 统计分析、机器学习、深度神经网络和计算机视觉。拟议的研究和 职业发展计划利用耶鲁大学提供的丰富资源，包括区域协调中心 NIH StrokeNet 中心、研究计算中心；高性能计算服务，以及 尖端的图像处理和分析基础设施。在本奖励期结束时，Payabvash 博士 将有能力成为一名独立资助的研究者，在以下领域进行高质量的研究 先进的神经影像技术和分析旨在改善脑血管患者的护理 疾病。