Clinical significance and genetic determinants of novel imaging measures of emphysema distribution in 9,743 smokers

项目摘要/摘要 候选人：Adel El Boueiz博士是一名肺部和重症监护内科医生兼科学家，完成了T32- 由钱宁网络医学部(CDNM)和哈佛医学院(HMS)资助。他 2016年5月获得HMS生物医学信息学医学硕士学位。他将被提拔 2017年7月1日在CDNM和HMS担任医学讲师。他的主要研究兴趣是 慢性阻塞性肺疾病的遗传流行病学及基因组翻译 临床实践和公共卫生方面的发现。他的长期目标是成为一名独立调查员， 在成像表型、基因组学和区域异质性预测分析方面的专业知识 COPD的各个方面(肺气肿、呼吸道疾病和肺血管重构)。 环境：El Boueiz博士将继续在富裕和 CDNM和布里格姆妇产科医院应用胸部影像实验室的多学科环境 (ACIL)。他将得到埃德温·K·西尔弗曼博士、彼得·J·卡斯塔尔迪博士和劳伊·L·圣何塞·埃斯特帕尔的指导， COPD定量成像、遗传流行病学和预测分析领域的领导者 指导年轻研究人员走向独立研究事业的跟踪记录。他的职业发展 还将由一个咨询委员会监督，该委员会拥有与他的提案的关键领域相关的专业知识。 研究：慢性阻塞性肺病是发病率和死亡率的主要原因，对公共卫生的重要性日益增加。 COPD是一种异质性疾病，这种异质性使确定慢性阻塞性肺疾病的预测因素变得复杂 疾病的发展，因此，有效疗法的发展。肺气肿的分布是一个 重要的COPD相关表型，可作为肺容量反应的有力预测因子 减少程序。尽管先进的基于纹理的CT量化方法可用，但全球 到目前为止，基于阈值的定量度量一直是放射学表征的基石 无法区分小叶中心型、全小叶型和间隔旁肺气肿的肺气肿分布 模式。在这个项目中，我们将应用一种基于纹理的CT量化方法来发现新的成像 大叶中央、全小叶和间隔旁肺气肿区域异质性的生物标志物 对特征良好的吸烟者进行队列调查，并使用全基因组测序确定他们的遗传决定因素 以及综合基因组学分析。结果将与其他丰富的表型一起考虑纳入 以及COPD疾病进展机器学习预测模型中的成像数据。 相关性：通过改进肺气肿分布的放射学表型，更好地了解 疾病病理生物学，以及更准确地预测疾病进展，拟议的工作将打开新的 开发个性化和改进的COPD治疗策略的研究途径。

PROJECT SUMMARY/ABSTRACT Candidate: Dr. Adel El Boueiz is a pulmonary and critical care physician-scientist completing a period of T32- funded support at the Channing Division of Network Medicine (CDNM) and Harvard Medical School (HMS). He received a Master's of Medical Science in Biomedical Informatics from HMS in May 2016. He will be promoted to Instructor of Medicine at the CDNM and HMS on July 1, 2017. His principal research interests are the genetic epidemiology of chronic obstructive pulmonary disease (COPD) and the translation of genomic discoveries into clinical practice and public health. His long-term goal is to be an independent investigator with expertise in imaging phenotyping, genomics, and predictive analytics of the regional heterogeneity of the various aspects of COPD (emphysema, airway disease, and pulmonary vascular remodeling). Environment: Dr. El Boueiz will continue to pursue his research and career development in the rich and multidisciplinary environment of the CDNM and the Brigham and Women's Hospital Applied Chest Imaging Lab (ACIL). He will be mentored by Drs. Edwin K. Silverman, Peter J. Castaldi, and Raúl San José Estépar, leaders in the field of COPD quantitative imaging, genetic epidemiology, and predictive analytics with excellent track records of mentoring young investigators towards independent research careers. His career development will also be overseen by an advisory committee with expertise related to key areas of his proposal. Research: COPD is a major cause of morbidity and mortality that is of increasing public health importance. COPD is a heterogeneous disease and this heterogeneity complicates the identification of the predictors of disease progression and consequently, the development of effective therapies. Emphysema distribution is an important COPD-related phenotype that emerged as a strong predictor of the response to lung volume reduction procedures. Despite the availability of advanced texture-based CT quantification methods, global threshold-based quantitative metrics have to date been the cornerstone for the radiological characterization of emphysema distribution with inability to differentiate centrilobular, panlobular, and paraseptal emphysema patterns. In this project, we will apply a texture-based CT quantification method to discover novel imaging biomarkers of the regional heterogeneity of centrilobular, panlobular, and paraseptal emphysema in a large cohort of well-characterized smokers and identify their genetic determinants using whole genome sequencing and integrative genomics analyses. The results will be considered for inclusion along with other rich phenotypic and imaging data in COPD disease progression machine learning predictive models. Relevance: Through improved radiographic phenotyping of emphysema distribution, better understanding of disease pathobiology, and more accurate prediction of disease progression, the proposed work will open new avenues of investigation for the development of personalized and improved COPD therapeutic strategies.