Airway Tree Subtyping on Large Cohorts of CT Images for COPD Risk

针对慢性阻塞性肺病 (COPD) 风险对大组 CT 图像进行气道树亚型分析

• 批准号: 10646297
• 负责人: Andrew Francis Laine
• 金额: $ 61.69万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646297
• 关键词: 3-Dimensional; Activities of Daily Living; Air Movements; Asthma; Biological; Caliber; Candidate Disease Gene; Cause of Death; Cessation of life; Chronic Obstructive Pulmonary Disease; Complex; Country; Creativeness; Detection; Development; Developmental Biology; Diagnostic tests; Dictionary; Disease Pathway; Disease susceptibility; Event; Exhibits; Fostering; Funding; General Population; Generations; Genetic; Grant; Health protection; High Resolution Computed Tomography; Image; Impairment; Individual; Intervention; Investigation; Knowledge; Label; Link; Longterm Follow-up; Lung; Machine Learning; Manuals; Mathematics; Measures; Minority; Mission; Modeling; Morphology; Multi-Ethnic Study of Atherosclerosis; National Heart, Lung, and Blood Institute; Obstructive Lung Diseases; Occupational; Outcome Measure; Pathway interactions; Phenotype; Prognosis; Public Health; Publishing; Quality of life; Radiology Specialty; Reproducibility; Research; Resolution; Respiratory Signs and Symptoms; Risk; Risk Factors; Role; Scanning; Severities; Shapes; Smoker; Smoking; Spirometry; Standardization; Structure; Testing; Textbooks; Time; Tobacco; Trees; United States; United States National Institutes of Health; Variant; Work; X-Ray Computed Tomography; airway obstruction; cigarette smoking; clinically relevant; clinically significant; cohort; cost; deep learning; detector; disease prognosis; disorder risk; environmental tobacco smoke; follow-up; genetic epidemiology; genome wide association study; high standard; improved; innovation; lung development; lung lobe; lung volume; machine learning method; never smoker; novel; personalized medicine; pollutant; prognostic significance; pulmonary function decline; respiratory; risk stratification; risk variant; supervised learning; tool; unsupervised learning

Project Summary / Abstract: Chronic obstructive pulmonary disease (COPD) defined by irreversible airflow limitation, is the 3rd leading cause of death globally and 4th in the United States. Smoking tobacco is a major extrinsic COPD risk factor, but despite six decades of declining smoking rates in many countries, the corresponding declines in COPD have been modest. Only a minority of lifetime smokers develop COPD, and up to 25% occurs in never smokers. While other factors have been linked to COPD much of the variation in COPD risk remains unexplained. In addition, personalized risk and therapies are lacking for COPD, due to a lack of reliable COPD subphenotypes. Airflow obstruction, or reduced airflow from the lungs, is determined in part by airway tree structure and lung volume, both of which can be imaged with high precision by high resolution computed tomographic (HRCT) scans. Emerging evidence by our group suggests that airway tree structure variation is common in the general population and is a major contributor to this unexplained COPD risk. By manual labeling of the airway tree structure, limited to one airway generation in just 2 of the 5 lung lobes (due to complexity of tree structure), we found that 26% of the general population has major airway branch variants that differ from the classical “textbook” structure, increase COPD risk, and have a strong and biologically plausible genetic basis. We further demonstrated that airway tree caliber variation (dysanapsis) measured on CT was a stronger predictor of COPD risk than all known risk factors including smoking. Yet there is no standardized approach to characterize the full scope airway tree variation, making the exact relationship between COPD and individual airway-structure features unclear. This proposal would apply for the first-time the power of machine learning methods to the entire airway tree structure imaged on HRCT to build logically upon prior high-impact work to discover new COPD subphenotypes for risk stratification and biological pathways of intervention. Also, we will apply sophisticated / rigorous mathematical clustering approaches to airway trees derived from over 18,000 computed tomography (CT) scans in three highly characterized NIH/NHLBI-funded cohorts – the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study, the Subpopulations and Intermediate Outcome Measures in Chronic Obstructive Pulmonary Disease Study (SPIROMICS), and the Genetic Epidemiology of COPD (COPDGene) Study, in addition to the Canadian Cohort of Obstructive Lung Disease (CanCOLD) – to discover and replicate novel and clinically significant airway tree subtypes and their genetic basis. The proposed study provides a transformative opportunity to define and validate normal and clinically relevant tree variation in the general population and COPD cohorts. This research would result in robust, reproducible, image based novel quantitative airway tree structure subtypes from lung CT scans, and understand their role in COPD risk, prognosis, and their underlying genetic basis to help personalize COPD risk.

项目摘要/摘要： 慢性阻塞性肺疾病 (COPD) 由不可逆气流受限定义，是第三大原因 死亡人数在全球排名第四，在美国排名第四。吸烟是慢性阻塞性肺病的一个主要外在危险因素，但是 尽管许多国家的吸烟率在过去 6 年来一直在下降，但慢性阻塞性肺病 (COPD) 的发病率也相应下降 一直很谦虚。只有少数终生吸烟者会患上慢性阻塞性肺病，而高达 25% 的慢性阻塞性肺病发生在从不吸烟的人身上。 尽管其他因素与慢性阻塞性肺病有关，但慢性阻塞性肺病风险的大部分变化仍然无法解释。在 此外，由于缺乏可靠的慢性阻塞性肺病亚表型，慢性阻塞性肺病缺乏个性化的风险和治疗。 气流阻塞或来自肺部的气流减少部分取决于气道树结构和肺部 体积，两者都可以通过高分辨率计算机断层扫描 (HRCT) 进行高精度成像 扫描。我们小组的新证据表明，气道树结构变异在一般情况下很常见 人口，是造成这种无法解释的慢性阻塞性肺病风险的主要因素。通过手动标记气道树 结构，仅限于 5 个肺叶中的 2 个肺叶中的一个气道生成（由于树结构的复杂性）， 我们发现 26% 的普通人群具有与经典气道分支不同的主要气道分支变异 “教科书”结构，增加慢性阻塞性肺病风险，并具有强大且生物学上合理的遗传基础。我们进一步 证明 CT 测量的气道树口径变化（休憩）是 COPD 的更强预测因子 风险高于包括吸烟在内的所有已知风险因素。但目前尚无标准化方法来表征 全方位气道树变异，使 COPD 与个体气道结构之间存在精确关系 特点不明确。该提案将首次将机器学习方法的力量应用于 在 HRCT 上成像的整个气道树结构，以逻辑方式建立在先前的高影响力工作的基础上，以发现新的 用于风险分层和干预生物学途径的慢性阻塞性肺病亚表型。 此外，我们还将对源自以下内容的气道树应用复杂/严格的数学聚类方法： 在 NIH/NHLBI 资助的三个高度特征化的队列中进行了超过 18,000 次计算机断层扫描 (CT) 扫描 – 动脉粥样硬化多种族研究 (MESA) 肺研究、亚群和中间结果 慢性阻塞性肺疾病研究 (SPIROMICS) 的措施以及遗传流行病学 慢性阻塞性肺病 (COPDGene) 研究，以及加拿大阻塞性肺疾病队列 (CanCOLD) – 发现和复制新颖且具有临床意义的气道树亚型及其遗传基础。 拟议的研究提供了一个变革性的机会来定义和验证正常和临床相关的 一般人群和慢性阻塞性肺病队列中的树变异。这项研究将产生稳健的、可重复的、 基于肺部 CT 扫描的新型定量气道树结构亚型的图像，并了解它们在 COPD 风险、预后及其潜在遗传基础有助于个性化 COPD 风险。