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

基本信息

批准号：
10299153
负责人：
Andrew Francis Laine
金额：
$ 68.14万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
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/10299153
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 Airway Disease Complex Country Detection Development Developmental Biology Diagnostic tests Dictionary Disease Pathway Disease susceptibility Event Exhibits Fostering Funding General Population Generations Genetic Gold Grant Health 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 Supervision Testing Textbooks Time Tobacco Trees United States United States National Institutes of Health Variant Work X-Ray Computed Tomography airway obstruction base cigarette smoking clinically relevant clinically significant cohort cost deep learning detector 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

项目摘要

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：由不可逆气流限制定义的慢性阻塞性肺疾病（COPD）是第三个主要原因全球死亡和美国第四。吸烟是主要的外部COPD危险因素，但尽管许多国家的吸烟率下降了六十年，但COPD的相应下降已经谦虚。只有少数终身吸烟者会出现COPD，而从未吸烟者中最多发生了25％。尽管其他因素已与COPD相关，而COPD风险的大部分变化仍然无法解释。在此外，由于缺乏可靠的COPD亚表型，COPD缺乏个性化风险和疗法。气流异常或肺部的气流减少，部分由气道树结构和肺确定卷，两者都可以通过高分辨率计算机断层扫描（HRCT）以高精度成像扫描。我们小组的新兴证据表明，气道树结构变化在一般中很常见人口，是这种意外的COPD风险的主要贡献者。通过气道树的手动标记结构仅限于5个肺部爱中的2个（由于树结构的复杂性），仅限于一次气道的生成我们发现26％的普通人群具有与经典不同的主要气道分支变体 “教科书”结构，增加COPD风险，并具有强大且生物学上合理的遗传基础。我们进一步证明在CT上测得的气道树口径变化（Dysanapsis）是COPD的更强预测指标风险比包括吸烟在内的所有已知风险因素。然而，没有标准化的方法来表征完整的示波器气道树的变化，使COPD与单个气道结构之间的确切关系功能不清楚。该建议将适用于第一次将机器学习方法的力量到达 HRCT上成像的整个气道树结构在先前的高影响力工作中逻辑上建立以发现新的 COPD亚表征用于风险分层和干预的生物学途径。此外，我们将应用复杂 /严格的数学聚类方法超过18,000次计算机断层扫描（CT）扫描三种高度特征的NIH/NHLBI资助的同类 - 动脉粥样硬化（MESA）肺部研究，亚群和中间结果的多种族研究慢性阻塞性肺部疾病研究（螺旋体）的措施，以及遗传流行病学除加拿大阻塞性肺疾病（Cancold）外，COPD（COPDGENE）研究 - 发现并复制新颖和临床意义的气道树亚型及其遗传基础。拟议的研究提供了定义和验证正常和临床相关的变革机会一般人群和COPD队列的树木变异。这项研究将导致坚固，可重复的基于图像的新颖定量气道树结构亚型来自肺CT扫描，并了解它们在 COPD风险，预后及其基本遗传基础，以帮助个性化COPD风险。