Novel Quantitative Emphysema Subtypes in MESA and SPIROMICS

描述（由申请人提供）：慢性阻塞性肺疾病（COPD）和肺气肿是美国第三大死亡原因。COPD和肺气肿被认为是异质性疾病，但其潜在机制尚未完全了解，并且缺乏肺气肿和COPD的精确的、基于生物学的和临床适用的定义。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）成像提供了丰富的肺实质，气道和脉管系统的体内信息，但这种信息的使用是有限的。当前基于CT的肺气肿量化和分型方法依赖于丢弃CT扫描中大部分空间信息的粗糙方法（例如阈值化）或基于监督学习的方法，所述监督学习简单地复制放射科医师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，并进行利用CT肺成像作为肺结构检查的新“显微镜”所需的临床相关性研究，并将定义定量肺气肿亚型（QES）用于临床和研究。为了达到这个目标，我们将：（1）开发新颖且稳健的图像处理工具，用于在数千次CT扫描上提取肺气肿放射学特征，（2）对大型梅萨患者队列执行肺气肿放射学特征的无监督聚类，以发现QES，（3）量化没有呼吸道症状和疾病的非吸烟“正常”参与者的同质人群中QES的正常水平。然后，我们将最迟从第3年开始，通过与症状、功能状态、呼吸结局（住院和死亡）相关来证明QES的临床相关性，并检查与基因型（全基因组单核苷酸多态性和外显子组罕见变异）的相关性。该分析的功效是通过利用来自正常志愿者和COPD患者的三个（梅萨、SPIROMICS、EMCAP）非常大的肺部研究队列（包含CT肺部图像、呼吸和遗传测量）来提供的。一般人群中QES的纵向进展超过10年（梅萨研究）和吸烟者中超过5年（EMCAP研究）将证实患者随访CT扫描的敏感性。最后，QES将被验证用于将肺气肿表型为具有重大预后意义、促成症状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，高效、低成本、可重复、全面且高度可转化为临床实践。

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.