Novel Quantitative Emphysema Subtypes in MESA and SPIROMICS

描述(申请人提供)：慢性阻塞性肺疾病(COPD)和肺气肿一起是美国的第三大死因。COPD和肺气肿被认为是异质性疾病，但其潜在的机制尚不完全清楚，也缺乏准确的、基于生物学的和临床适用的肺气肿和COPD的定义。特别是，已发现肺气肿的某些亚型与不同的风险因素相关，因此可能代表不同的疾病。计算机断层扫描(CT)成像提供了关于肺实质、呼吸道和血管系统的丰富的活体信息，但这些信息的使用有限。目前基于CT的肺气肿量化和分型方法要么依赖于在CT扫描中丢弃大部分空间信息的原始方法(例如阈值)，要么依赖于基于监督学习的方法，该方法简单地复制放射科医生的解释或使用生理测量作为标签。该项目将开发缺失的计算工具，并进行临床相关性研究，将CT肺部成像作为一种新的用于肺结构检查的‘显微镜’，并将定义用于临床和研究的定量肺气肿亚型(QES)。为了实现这一目标，我们将：(1)开发新的和强大的图像处理工具，以提取数以千计的CT扫描的肺气肿放射学特征；(2)对大量台地患者的肺气肿放射学特征进行无监督聚类，以发现QES；以及(3)量化无呼吸道症状和疾病的非吸烟“正常”参与者的同种人群中QES的正常水平。然后，我们将最迟从第三年开始，通过与症状、功能状态、呼吸结果(住院和死亡)的相关性来证明QES的临床相关性，并检查与基因的相关性(全基因组范围的单核苷酸多态和外显性稀有变异)。分析的力量来自三个非常大的肺部研究队列(MESA、SPIROMICS、EMCAP)--包含来自正常志愿者和COPD患者的CT肺部图像、呼吸和遗传测量。在普通人群中，QES的纵向进展超过10年(MESA研究)，在吸烟者中超过5年(EMCAP研究)，将证实患者CT扫描随访的敏感性。最终，QES将被验证用于将肺气肿分型为具有重大预后意义、对症状有贡献并具有遗传基础的亚型。因此，拟议的QES源自自动化处理工具，是高效、低成本、可重复、全面的，并可高度转化为临床实践。

DESCRIPTION (provided by applicant): Chronic obstructive pulmonary disease (COPD) and emphysema together are the third leading cause of death in the United States. COPD and emphysema are recognized to be heterogeneous diseases, but its underlying mechanisms are not fully understood and there is a lack of precise, biologically based and clinically applicable definitions of emphysema and COPD. In particular, certain subtypes of emphysema have been found to correlate with different risk factors and are therefore likely to represent different diseases. Computed tomography (CT) imaging provides rich in vivo information on the lung parenchyma, airways and vasculature but use of this information is limited. Current approaches for CT-based emphysema quantification and subtyping rely on either crude methods that discard most of the spatial information in CT scans (e.g. thresholding) or on approaches based on supervised learning that simply replicate radiologists' interpretation or use physiological measures as labels. This project will develop the missing computational tools and perform the clinical correlation studies required to exploit CT lung imaging as a new 'microscope' for lung structure examination and will define quantitative emphysema subtypes (QES) for clinical and research use. To reach this goal, we will: (1) develop novel and robust image processing tools for the extraction of emphysema radiological features on thousands of CT scans, (2) perform unsupervised clustering of emphysema radiological features on the large MESA cohort of patients to discover QES and, (3) quantify the levels of normality for QES among homogeneous populations of non-smoking "normal" participants without respiratory symptoms and disease. We will then, starting at the latest in Year 3, demonstrate the clinical relevance of the QES by correlating with symptoms, functional status, respiratory outcomes (hospitalization and death) and examine associations with genotype (genome-wide single nucleotide polymorphisms and exomic rare variants). The power of the analysis is provided by the exploitation of three (MESA, SPIROMICS, EMCAP) very large cohorts of Lung studies - containing CT lung images, respiratory and genetic measures - from normal volunteers and COPD patients. Longitudinal progression of QES in the general population over 10 years (MESA study) and over 5 years among smokers (EMCAP study) will confirm the sensitivity of patients' follow up with CT scans. At the end, QES will be validated to be used to phenotype emphysema into subtypes that have major prognostic significance, contribute to symptoms and have a genetic underpinning. The proposed QES, being derived from automated processing tools, are therefore efficient, low-cost, reproducible, comprehensive, and highly translatable into clinical practice.