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：慢性阻塞性肺疾病(COPD)是 并与严重的呼吸道疾病发病率有关。慢性阻塞性肺疾病的特征是肺活量 由肺实质(肺气肿)和呼吸道的结构变化引起的气流阻塞。然而，在那里 肺活量测定诊断与CT上肺气肿的存在明显不一致。 吸气CT上肺气肿的定义是低密度区&-950 Hounsfield单位(HU)。通过 通过图像配准对吸气和呼气CT扫描进行解剖匹配，得出CT测量方法 称为肺变形雅可比决定因子(J)的肺弹性，它是逐点测量 呼吸过程中肺的扩张和收缩。我们假设基于CT的肺力学将使 根据传统的CT密度标准确定看起来正常但机械受损的区域 在呼吸过程中。我们将通过评估10,300名现在和以前的吸烟者来检验“无声区”假说 加入COPD遗传流行病学(COPD基因)队列，有以下具体目标。在目标1中， 我们将通过匹配吸气和呼气CT扫描来量化静止区，并确定它们之间的关联 具有肺功能、呼吸生活质量和功能能力。在目标2中，我们将使用6284名受试者 在5年后完成第二次COPD基因访问，以量化进入静默区的百分比 通过检测静止区与肺气肿的相关性来确定静止区的预后价值 伴随着FEV1的下降和死亡率。在目标3中，我们将开发一个深度卷积神经网络来识别Silent 从吸气CT扫描中直接识别区域，从而避免了计算密集的图像匹配过程。 我将利用这项提议获得生物统计学、肺生理学、深度学习、 用于大型医学队列的并行计算。这一职业发展奖创造的机会将 为我在慢性阻塞性肺病领域获得专业知识和开发研究利基提供了一条清晰的道路。 这项研究计划和职业发展计划的目标可以通过 苏里亚·巴特博士是肺成像研究领域的领先专家，也是UAB肺部成像实验室的主任和Dr。 计算机视觉、图像配准和机器学习方法方面的专家阿里·纳赫马尼说。这个 拟议的学习将为我提供技能，以实现我独立职业生涯的长期目标 专注于慢性阻塞性肺疾病的医学成像和机器学习应用的翻译研究。