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)是一种高度流行的异质性疾病， 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 特点。由于标准表型方法的局限性，分期相似的COPD患者 可能表现出截然不同的进展模式。小气道病(SAD)，一种可治疗但神秘的疾病 慢性阻塞性肺疾病的组成部分，是COPD早期表现出气流阻塞的重要因素。近年来， SAD被认为是肺实质不可逆转破坏的前兆，即 肺气肿。预测SAD是否以及何时会导致肺气肿的能力将立即产生临床效果 对慢性阻塞性肺疾病患者护理的影响。2012年，我们报告了参数响应图(PRM)分析 当应用于成对的吸气和呼气CT扫描时，能够同时进行 可视化和量化单个COPD患者的“功能性”SAD(FSAD)和肺气肿的程度。 从那时起，我们取得了三个关键进展：第一，PRM衍生的fSAD可以预测肺活量下降。 COPD患者和肺气肿的发展；第二，我们已经在人类肺样本中验证了PRM- 导出的fSAD是小气道狭窄和丢失的衡量标准；最后，应用技术捕获 肺内fSAD的区域性差异，我们增强了PRM(拓扑性PRM[tPRM])，以提供更多 对当地疾病严重程度的敏感衡量比最初的PRM概念可能的要好。基于我们的 结果，我们假设PRM或其高级形式的TPRM具有预测长期患者的潜力 进步。该提案的目标将是使用基线、第5年和最近可用的第10年COPDgene 通过三个具体目标确定PRM预测疾病进展的能力的数据：1)特征 PRM得出的5年和10年fSAD进展模式；2)确定地区差异如何 疾病分布，如tPRM所确定的，识别局部肺气肿的区域性发病；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 轨迹。我们期望PRM指标将确定COPD患者有更快患病的风险 但利用区域信息和机器学习策略将进一步增强我们的 接近。这样的分析结果既可以确定患者是否适合进行更密集、更有针对性的治疗 在疾病的早期阶段进行治疗，有助于我们了解小气道疾病的进展 和慢性阻塞性肺病的肺气肿。