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

项目总结/摘要 候选人：阿德尔El Boueiz博士是一名肺科和重症监护医生兼科学家，完成了T32- 在网络医学的钱宁分部（CDNM）和哈佛医学院（HMS）的资助支持。他 于2016年5月获得HMS生物医学信息学医学科学硕士学位。合格者给予晋升 2017年7月1日在CDNM和HMS的医学讲师。他的主要研究兴趣是 慢性阻塞性肺疾病（COPD）的遗传流行病学和基因组翻译 临床实践和公共卫生的新发现。他的长期目标是成为一名独立调查员， 在成像表型，基因组学和预测分析的区域异质性的专业知识， COPD的各个方面（肺气肿、气道疾病和肺血管重塑）。 环境：El Boueiz博士将继续在富人和穷人中从事研究和职业发展， CDNM和布里格姆妇女医院应用胸部成像实验室的多学科环境 （ACIL）。他将由Edwin K博士指导。Silverman，Peter J. Castaldi，and Raúl San José Estépar， 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生涯的记录。他的职业发展 还将由一个具有与其提案关键领域相关专业知识的咨询委员会监督。 研究：COPD是发病率和死亡率的主要原因，其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COPD的预测因子的识别变得复杂。 疾病进展，从而开发有效的治疗方法。肺气肿分布是一个 重要的COPD相关表型，作为肺容量反应的强预测因子出现 减少程序。尽管有先进的基于纹理的CT量化方法， 迄今为止，基于阈值的定量度量已经成为放射学表征的基石， 肺气肿分布，无法区分小叶中心、全小叶和间隔旁肺气肿 模式.在这个项目中，我们将应用基于纹理的CT量化方法来发现新的成像 在一个大的肺组织中， 一组特征明确的吸烟者，并使用全基因组测序确定其遗传决定因素 和综合基因组学分析。将考虑将结果沿着纳入其他丰富的表型 和成像数据在COPD疾病进展机器学习预测模型中的应用。 相关性：通过改善肺气肿分布的放射学表型， 疾病病理生物学，更准确地预测疾病进展，拟议的工作将开辟新的 研究途径，以开发个性化和改进的COPD治疗策略。

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.