Silent Zones of Lung Disease in COPD

慢性阻塞性肺病 (COPD) 肺部疾病的静默区

• 批准号: 10590542
• 负责人: Sandeep Bodduluri
• 金额: $ 16.84万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590542
• 关键词: Acceleration; Activities of Daily Living; Affect; Agreement; Anatomy; Area; Biological Markers; Biomechanics; Biometry; Cause of Death; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Complement; Complex; Computer Vision Systems; Data; Data Set; Development; Development Plans; Diagnosis; Disease Progression; Dyspnea; Early Diagnosis; Elasticity; Enrollment; Goals; Health Care Costs; Image; Impairment; Individual; K-Series Research Career Programs; Label; Lung; Lung diseases; Machine Learning; Measures; Mechanics; Medical; Medical Imaging; Mentorship; Nature; Neural Network Simulation; Outcome; Persons; Physiology; Process; Pulmonary Emphysema; Pulmonary Function Test/Forced Expiratory Volume 1; Quality of life; Questionnaires; Research; Research Proposals; Respiration; Respiratory Signs and Symptoms; Scanning; Semantics; Severities; Smoker; Spirometry; Stress; Structure of parenchyma of lung; Testing; Tissue Expansion; Tissues; Training; Translational Research; United States; Visit; Walking; X-Ray Computed Tomography; airway obstruction; career; career development; clinical diagnosis; clinical practice; clinically significant; cohort; cone-beam computed tomography; convolutional neural network; deep learning; deep neural network; density; diagnosis standard; early detection biomarkers; follow-up; former smoker; genetic epidemiology; high risk; image registration; imaging biomarker; improved; inflammatory lung disease; lung basal segment; lung imaging; machine learning method; mortality; neural network architecture; novel; parallel computer; prognostic; prognostic value; pulmonary function; pulmonary function decline; quantitative imaging; respiratory; respiratory health; respiratory morbidity; skills; small airways disease; statistics; tool

Project Summary: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and is associated with substantial respiratory morbidity. COPD is characterized by spirometric airflow obstruction due to structural changes in lung parenchyma (emphysema) and airways. However, there exists a marked discordance between spirometry diagnosis and presence of emphysema on CT. Emphysema on inspiratory CT is defined by low-density areas <-950 Hounsfield Units (HU). By anatomically matching inspiratory and expiratory CT scans through image registration, we derived a CT measure of lung elasticity termed the Jacobian determinant of lung deformation (J) which is a point-by-point measure of lung expansion and contraction during respiration. We hypothesize that the CT-based lung mechanics will enable identification of regions that appear normal per traditional CT density criteria but are mechanically compromised during respiration. We will test the “Silent Zones” hypothesis by evaluating 10,300 current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) cohort with the following specific aims. In Aim 1, we will quantify Silent Zones by matching inspiratory and expiratory CT scans and to determine their associations with lung function, respiratory quality of life and functional capacity. In Aim 2, we will use 6,284 subjects who completed a second COPDGene visit after 5-years to quantify the percentage of Silent Zones progressed into emphysematous areas and also to determine the prognostic utility of Silent Zones by testing their association with FEV1 decline and mortality. In Aim 3, we will develop a deep convolutional neural network to identify Silent Zones directly from inspiratory CT scans, thus avoiding the computationally intensive image matching process. I will utilize this proposal to acquire advanced training in biostatistics, lung physiology, deep learning, parallel computing for large medical cohorts. The opportunities created by this Career Development Award will provide me with a clearly delineated path to acquire expertise and develop a research niche in the field of COPD. The aims of this research proposal and career development plan are possible through the active mentorship of Dr. Surya Bhatt, a leading expert in lung imaging research and the Director of UAB Lung Imaging Lab and Dr. Arie Nakhmani, an expert in computer vision, image registration, and machine learning methodologies. The proposed study will provide me with the skill set to achieve my long-term goal of an independent career in translational research focusing on medical imaging and machine learning applications for COPD.

项目摘要：慢性阻塞性肺部疾病（COPD）是死亡的第四个主要原因 美国，与大量呼吸道发病有关。 COPD的特征是肺活量测定 由于肺实质（肺气肿）和气道的结构变化引起的气流异议。但是，那里 肺活量测定法与CT上肺气肿的存在之间存在明显的不一致。 灵感CT上的肺气肿由低密度区域<-950 Hounsfield单元（HU）定义。经过 通过图像注册，我们得出了CT测量的解剖学和呼气CT扫描 肺部变形（J）的肺弹性决定因素（J）的决定因素，这是对点的逐点测量 呼吸过程中的肺部扩张和收缩。我们假设基于CT的肺力学将启用 根据传统的CT密度标准，鉴定出正常的区域，但被机械损害 在呼吸过程中。我们将通过评估10,300名当前和前吸烟者来检验“无声区域”假设 以以下特定目的招募了COPD（COPDGENE）队列的遗传流行病学。在AIM 1中， 我们将通过匹配灵感和呼气CT扫描并确定其关联来量化无声区域 具有肺功能，呼吸质量和功能能力。在AIM 2中，我们将使用6,284个主题 在5年后完成了第二次COPDGENE访问，以量化寂静区的百分比发展到 强度区域，并通过测试其关联来确定无声区域的预后效用 FEV1下降和死亡率。在AIM 3中，我们将开发一个深层的卷积神经网络来识别沉默 直接来自Insperation CT扫描的区域，从而避免了计算密集型图像匹配过程。 我将利用该建议来获得生物统计学，肺部生理学，深度学习， 大型医疗队列的平行计算。这个职业发展奖创建的机会将 为我提供一条明确的划定的途径，以获取专业知识并在COPD领域开发研究细分市场。 这项研究建议和职业发展计划的目的是通过积极的心态 肺成像研究领先专家，UAB肺成像实验室主任Surya Bhatt博士和博士 Arie Nakhmani，计算机视觉，图像注册和机器学习方法的专家。这 拟议的研究将为我提供技能，以实现我的长期目标 转化研究重点是COPD的医学成像和机器学习应用。