Prognostic Markers of Emphysema Progression

项目摘要/摘要 慢性阻塞性肺疾病(COPD)在美国影响着多达2400万人，目前 预计到2020年将成为全球第三大死亡原因，总成本为500亿美元。慢性阻塞性肺疾病 传统上分为肺气肿和慢性支气管炎的临床表型，但其 人们对潜在的机制知之甚少。特别是，肺气肿被定义为异常的、永久性的 远端空隙扩张。这一病理过程的发展和进展是相关的。 伴有肺功能下降和进行性临床损害。计算机体层摄影(CT)成像 胸部越来越多地被用来客观地量化疾病及其进展。当前 量化肺气肿进展的方法是有限的，并且抛弃了大部分的空间和时间 CT扫描信息在吸气和呼气时获得。在这份提案中，我们计划开发 基于图像密度标记物预测肺气肿进展的计算组件 肺机械应变特性取决于其潜在的肺气肿亚型。这项建议 利用我们以前在计算肺气肿亚型方面的经验来发现、验证和翻译 围绕肺气肿进展的假设机制量身定做的一组新的预后标志物： 炎症损伤和机械劳损。为了达到这个目标，我们将(1)发展成一种晚期肺气肿 采用新型深度学习结构的子分型方法，(2)提出了一种快速的质量保持大数据量算法 能够发现肺组织之间的局部弹性特性的位移配准方法 吸气和呼气CT扫描，(3)基于图像发现新的亚型特异性生物标志物特征 使用无监督深度学习技术的密度关系和力学特性 统计框架，以及(4)验证所提出的生物标志物及其相关性的预后价值 随着终点和临床结果的下降，使其能够进行临床解释和翻译。除了……之外 这将探索基于先进的机器学习技术和 进行了一项模型比较研究，以确定最能预测肺气肿进展的模型。我们的 分析将处理对应于5517名受试者的12,300次扫描，这些扫描具有来自 COPD基因队列-COPD患者中最大的队列之一，包含吸气和呼气时的CT图像， 呼吸和基因测量。拟议的方法将提供可重复性、自动化和 低成本预测肺气肿进展的体内生物标志物可能使新的发现成为可能 并将其转化为临床实践。

Project Summary/Abstract Chronic Obstructive Pulmonary Disease (COPD) affects up to 24 million people in the United States and is projected to be the 3rd leading cause of death worldwide by 2020 with a total cost of $50 billion. COPD has been traditionally dichotomized into the clinical phenotypes of emphysema and chronic bronchitis, but its underlying mechanisms are poorly understood. In particular, emphysema is defined as abnormal, permanent dilation of the distal airspaces. The development and progression of this pathologic process are associated with a decline in lung function and progressive clinical impairment. Computed tomographic (CT) imaging of the chest is increasingly being leveraged to quantify the disease and its progression objectively. Current approaches to quantify emphysema progression are limited and discard most of the spatial and temporal information in CT scans obtained at inspiration and expiration. In this proposal, we plan on developing computational components to prognosticate emphysema progression that builds upon image density markers and lung mechanical strain characteristics conditioned on their underlying emphysema subtypes. This proposal leverages our previous experience in computational emphysema subtyping to discover, validate and translate a novel panel of prognostic markers tailored around the postulated mechanisms of emphysema progression: inflammation injury and mechanical strain. To reach this goals, we will (1) develop an advanced emphysema subtyping approach using novel deep learning architectures, (2) develop a fast mass preserving large displacement registration approach to enable the discovery of local elastic properties of lung tissue between inspiratory and expiration CT scans, (3) discover new subtype-specific biomarker features based on image density relations and mechanical properties using unsupervised deep learning techniques within a common statistical framework, and (4) validate the prognostic value of the proposed biomarkers and their association with decline end-points and clinical outcomes to enable its clinical interpretation and translation. In addition to that, will be explored alternative prognostic models based on advanced machine learning techniques and performed a model comparison study to define the most prognostic model for emphysema progression. Our analysis will process 12,300 scans corresponding to 5,517 subjects with baseline and follow-up data from the COPDGene cohort –one of the largest cohort in COPD containing CT images at inspiration and expiration, respiratory and genetic measurements. The proposed methodology will provide reproducible, automatic and low-cost prognostic in-vivo biomarkers of emphysema progression that may enable the discovery of new therapies and translate them into clinical practice.