Prognostic Markers of Emphysema Progression

肺气肿进展的预后标志物

基本信息

批准号：
10368048
负责人：
Raul San Jose Estepar
金额：
$ 68.8万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-15 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10368048
关键词：
Address Affect Alveolar wall Appearance Architecture Biological Markers Cause of Death Characteristics Chest Chronic Chronic Bronchitis Chronic Obstructive Pulmonary Disease Clinical Clinical stratification Complement Connective Tissue Data Deposition Development Disease Disease Progression Distal Fracture Genetic Goals Histologic Image Impairment Inflammation Inflammation Process Inflammatory Injury Investigation Joints Lead Logistic Regressions Lung Lung volume reduction surgery Machine Learning Maps Measurement Mechanical Stress Mechanics Mediating Medical Genetics Methodology Modeling Outcome Pathologic Processes Pathway interactions Patient risk Patients Pattern Persons Plasma Predisposition Process Prognostic Marker Property Pulmonary Emphysema Radiology Specialty Regression Analysis Reporting Reproducibility Research Risk Scanning Smoker Staging Structure of parenchyma of lung Supervision Techniques Therapeutic Tissues Translating Translations United States Validation X-Ray Computed Tomography base clinical application clinical phenotype clinical practice cohort convolutional neural network cost deep learning density experience expiration follow-up functional decline genetic association improved in vivo inclusion criteria inflammatory marker insight inspiration expiration mechanical properties novel novel therapeutics patient stratification preservation prognostic prognostic model prognostic value prognostication progression marker prospective pulmonary function reduce symptoms respiratory response response to injury risk stratification specific biomarkers tissue stress tobacco smoke exposure

项目摘要

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.

项目概要/摘要慢性阻塞性肺疾病 (COPD) 影响着美国多达 2400 万人，并且预计到 2020 年，慢性阻塞性肺病将成为全球第三大死因，总成本达 500 亿美元。传统上将其分为肺气肿和慢性支气管炎的临床表型，但其其潜在机制尚不清楚，尤其是肺气肿被定义为异常的、永久性的。这种病理过程的发生和进展是相关的。肺功能下降和进行性计算机断层扫描 (CT) 成像。目前，胸部越来越多地被用来客观地量化疾病及其进展。量化肺气肿进展的方法是有限的，并且放弃了大部分空间和时间在本提案中，我们计划开发在吸气和呼气时获得的 CT 扫描信息。基于图像密度标记来预测肺气肿进展的计算组件和肺机械应变特征取决于其潜在的肺气肿亚型。利用我们之前在计算肺气肿亚型方面的经验来发现、验证和转化围绕肺气肿进展的假设机制定制的一组新颖的预后标志物：为了实现这一目标，我们将 (1) 发展为晚期肺气肿。使用新颖的深度学习架构的子类型方法，（2）开发一种快速质量保存大位移记录方法能够发现肺组织之间的局部弹性特性吸气和呼气 CT 扫描，(3) 基于图像发现新的亚型特异性生物标志物特征在共同的范围内使用无监督深度学习技术来计算密度关系和机械性能统计框架，以及（4）验证所提出的生物标志物及其关联的预后价值具有下降的终点和临床结果，以实现其临床解释和翻译。将探索基于先进机器学习技术的替代预测模型进行了模型比较研究，以确定肺气肿进展的最具预后性的模型。分析将处理对应于 5,517 名受试者的 12,300 次扫描，其基线和后续数据来自 COPDGene 队列 – COPD 中最大的队列之一，包含吸气和呼气时的 CT 图像，所提出的方法将提供可重复的、自动的和遗传的测量。肺气肿进展的低成本体内预后生物标志物可能有助于发现新的疗法并将其转化为临床实践。