Characterization of Early Response to Chronic Lung Injury using Chest CT

项目摘要 在一些人中，长期吸烟会导致肺气肿和肺纤维化。 这两种实质改变都是不可逆转的，突显了早期识别 他们的发展和进步。不幸的是，目前可用的检测存在的方法和 这些变化的演变对于非常早期的疾病和微妙的疾病的敏感性和特异性是有限的。 进步。Ash博士的工作表明，计算机断层扫描(CT)的自动客观分析 胸部扫描可以发现临床上相关的放射学发现，称为间质改变，可能 代表早期肺纤维化，即使在没有明显疾病的个体中也是如此。在这个项目的第一个目标中 Ash博士将改进并利用一种更敏感、更具体的自动化CT分析工具，他和 他的实验室已经开发用于检测肺气肿和间质变化。他将决定是否 用这种方法检测的肺气肿和间质改变在这些患者中具有临床意义。 以前执行的目测分析认为是正常的，其他目标认为是正常的 接近了。在第二个目标中，他将确定局部高密度组织区域是否视觉正常 使用他的客观分析工具的增强版本测量的看起来像肺实质是相关的 包括死亡率、其他临床结果和外周炎症指标。最后，在第三个目标中，他 将利用这些技术分析从COPDgene研究中获得的纵向CT扫描 超过10年的跟踪，并将确定预测或修改发展和进展的因素 CT上可见实质性改变。 Ash医生将在Brigham And的肺和重症监护医学部执行这项工作 妇女医院(BWH)是哈佛医学院的核心教学医院，由Dr。 乔治·沃什科，医学图像分析领域的专家，也是 BWH应用胸腔成像实验室。在沃什科博士和他的科学顾问的指导下 委员会，Ash博士制定了一个全面的五年培训计划，以发展所需的技能 成为具有定量图像分析专业知识的独立调查员，包括预测 建模和统计机器学习。 阿什博士致力于学术医学的事业。他的目标是成为一名临床医生和科学家 在此奖项期间获得的技能，以提高我们检测和监测吸烟相关肺部疾病的能力。 他提出的技术可能有助于识别与吸烟相关的可改变的风险因素和治疗方法 肺部疾病，确定哪些患者可能从治疗中受益，并监测对 心理治疗。

Project Summary In some individuals chronic tobacco smoke exposure results in emphysema and pulmonary fibrosis. Both of these parenchymal changes are irreversible, highlighting the importance of the early identification of their development and progression. Unfortunately, currently available methods for detecting the presence and evolution of these changes have limited sensitivity and specificity for very early disease and for subtle disease progression. Dr. Ash’s work has shown that automated objective analysis of computed tomography (CT) scans of the chest can detect clinically relevant radiologic findings called interstitial changes that may represent early pulmonary fibrosis, even in individuals without visually apparent disease. In the first aim of this proposal, Dr. Ash will refine and utilize a more sensitive and specific automated CT analysis tool that he and his lab have developed for the detection of both emphysema and interstitial changes. He will determine if emphysema and interstitial changes detected using this method are clinically significant in those patients deemed normal by previously performed visual analysis and in those deemed normal by other objective approaches. In the second aim, he will determine if areas of locally high density tissue in visually normal appearing lung parenchyma measured using augmented versions of his objective analysis tools are associated with mortality, other clinical outcomes, and peripheral measures of inflammation. Finally, in the third aim he will utilize these techniques to analyze longitudinal CT scans from the COPDGene study that were obtained over 10 years of follow-up, and will identify factors that predict or modify the development and progression of parenchymal changes on CT. Dr. Ash will perform this work in the Division of Pulmonary and Critical Care Medicine at Brigham and Women’s Hospital (BWH), a core teaching hospital of Harvard Medical School, under the mentorship of Dr. George Washko, an expert in the field of medical image analysis and the co-principal investigator of the Applied Chest Imaging Laboratory at BWH. With the guidance of Dr. Washko and his scientific advisory committee, Dr. Ash has developed a comprehensive five year training program to develop the skills needed to become an independent investigator with expertise in quantitative image analysis, including predictive modeling and statistical machine learning. Dr. Ash is dedicated to a career in academic medicine. His goal is to become a clinician-scientist using the skills gained during this award to improve our ability to detect and monitor smoking related lung disease. The techniques he has proposed may help identify modifiable risk factors and treatments for smoking related lung disease, determine which patients are likely to benefit from treatment, and monitor the response to therapy.