Prediction of COPD Progression by PRM

通过 PRM 预测 COPD 进展

• 批准号: 10579311
• 负责人: Craig J Galban
• 金额: $ 68.62万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579311
• 关键词: 3-Dimensional; American; Architecture; Characteristics; Chest imaging; Chronic Care; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Data; Cohort Studies; Data; Decision Trees; Development; Diameter; Disease; Disease Marker; Disease Progression; Early identification; Exhibits; Funding; Goals; Heterogeneity; Histologic; Human; Image; Individual; Lesion; Localized Disease; Lung; Machine Learning; Maps; Measures; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Neural Network Simulation; Outcome; Outcome Study; Pathologic; Patients; Pattern; Phenotype; Physicians; Pulmonary Emphysema; Reporting; Research; Risk; Sampling; Scanning; Sensitivity and Specificity; Severity of illness; Site; Smoker; Spirometry; Staging; Structure of parenchyma of lung; Symptoms; Techniques; Testing; Therapeutic Trials; Variant; Visit; Visualization; X-Ray Computed Tomography; airway obstruction; analytical tool; cohort; concept mapping; convolutional neural network; deep learning; disease classification; disorder risk; effective therapy; expectation; image registration; imaging biomarker; improved; information model; learning strategy; mortality; non-invasive imaging; novel; predictive modeling; pulmonary function; pulmonary function decline; radiological imaging; regional difference; response; risk prediction; small airways disease; statistical learning; targeted treatment

PROJECT ABSTRACT Chronic obstructive pulmonary disease (COPD) is a highly prevalent and heterogeneous disorder that afflicts nearly 30 million Americans. Current disease staging and therapy is based primarily on spirometry and clinical characteristics. Due to limitations in the standard phenotyping approaches, patients with similarly staged COPD may exhibit strikingly different progression patterns. Small airways disease (SAD), a treatable but occult component of COPD, is a significant contributor to airflow obstruction manifesting early in COPD. In recent years, SAD has been has been implicated as a precursor to the irreversible destruction of lung parenchyma, i.e. emphysema. The ability to predict if and when SAD will lead to emphysema would have an immediate clinical impact on the care of COPD patients. In 2012 we reported on the Parametric Response Map (PRM) analytical technique that when applied to paired inspiratory and expiratory CT scans is capable of simultaneously visualizing and quantifying the extent of “functional” SAD (fSAD) and emphysema in a single COPD patient. Since then we have made three key advances: first, PRM-derived fSAD is predictive of spirometric decline in COPD patients and emphysema development; second, we have validated in human lung samples that PRM- derived fSAD is a measure of small airway narrowing and loss; and finally, applying techniques to capture regional variation of fSAD within the lung, we have enhanced PRM (topological PRM [tPRM]) to provide a more sensitive measure of local disease severity than what is possible with the original PRM concept. Based on our findings, we postulate that PRM, or its advanced form tPRM, has the potential to predict long-term patient progression. The goal of this proposal will be to use baseline, Year 5 and recently available Year 10 COPDGene data to determine the ability of PRM to predict disease progression through three Specific Aims: 1) Characterize PRM-derived fSAD progression patterns over a 5 and 10 year period; 2) Determine how regional differences in disease distribution, as determined by tPRM, identify regional onset of local emphysema; and 3) Apply machine learning strategies to PRM/tPRM and other clinical metrics to develop models that predict patient disease trajectories. It is our expectation that PRM metrics will identify COPD patients at risk for more rapid disease progression but that utilizing regional information and machine learning strategies will further enhance our approach. The results of such analyses could both identify patients appropriate for more intense, targeted therapy at an early disease stage and contribute to our understanding of the progression of small airways disease and emphysema in COPD.

项目摘要 慢性阻塞性肺疾病 (COPD) 是一种高度流行且具有异质性的疾病，困扰着许多人 近 3000 万美国人目前的疾病分期和治疗主要基于肺活量测定和临床。 由于标准表型分析方法的局限性，患有类似分期 COPD 的患者。 小气道疾病（SAD）可能表现出截然不同的进展模式，这是一种可治疗但隐匿的疾病。 近年来，COPD 的一个重要组成部分是导致 COPD 早期出现的气流阻塞。 SAD 被认为是肺实质不可逆破坏的前兆，即 预测 SAD 是否以及何时会导致肺气肿的能力将具有直接的临床意义。 2012 年，我们报告了参数反应图 (PRM) 分析。 该技术应用于配对吸气和呼气 CT 扫描时能够同时进行 对单个 COPD 患者的“功能性”SAD (fSAD) 和肺气肿的程度进行可视化和量化。 从那时起，我们取得了三个关键进展：首先，PRM 衍生的 fSAD 可以预测肺活量下降 COPD 患者和肺气肿的发展；其次，我们在人类肺部样本中验证了 PRM- 导出的 fSAD 是小气道狭窄和损失的衡量标准，最后应用捕获技术； 由于 fSAD 在肺内的区域差异，我们增强了 PRM（拓扑 PRM [tPRM]）以提供更多信息 根据我们的原始 PRM 概念，可以更灵敏地衡量局部疾病的严重程度。 根据研究结果，我们假设 PRM 或其高级形式 tPRM 有潜力预测长期患者 该提案进展的目标是使用基线、第 5 年和最近可用的第 10 年 COPDGene 数据来确定 PRM 通过三个具体目标预测疾病进展的能力：1) 表征 PRM 衍生的 5 年和 10 年期间的 fSAD 进展模式 2) 确定区域差异如何； 根据 tPRM 确定的疾病分布，确定局部肺气肿的区域发作；以及 3) 应用机器 学习 PRM/tPRM 和其他临床指标的策略，以开发预测患者疾病的模​​型 我们期望 PRM 指标能够识别出有加速疾病风险的 COPD 患者。 但利用区域信息和机器学习进展策略将进一步增强我们的能力 此类分析的结果都可以识别出适合更强烈、更有针对性的患者。 在疾病早期阶段进行治疗，有助于我们了解小气道疾病的进展 和慢性阻塞性肺病（COPD）中的肺气肿。