Robust Clinical Translation of CT Imaging Biomarker in COPD for EBV Patient Selection

COPD 中 CT 成像生物标志物的稳健临床转化，用于 EBV 患者选择

• 批准号: 10378091
• 负责人: MATTHEW S BROWN
• 金额: $ 67.35万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10378091
• 关键词: Accounting; Affect; Air; Biological Markers; Blood Vessels; Cause of Death; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Trials; Collaborations; Computer Vision Systems; Data; Data Set; Development; Devices; Disease; Effectiveness; Eligibility Determination; FDA approved; Fissural; Goals; Heterogeneity; Image; Image Analysis; Life Style; Lobar; Lobe; Lung; Lung Transplantation; Lung Volume Reductions; Measures; Methods; Modeling; Morbidity - disease rate; Outcome; Pathway interactions; Patient Selection; Patients; Perfusion; Progressive Disease; Protocols documentation; Pulmonary Emphysema; Recording of previous events; Research; Severities; Severity of illness; Shortness of Breath; Standardization; Structure of parenchyma of lung; Testing; United States; Universities; Work; X-Ray Computed Tomography; clinical translation; density; disease heterogeneity; imaging biomarker; implantation; improved; inclusion criteria; machine learning method; mortality; mortality risk; predicting response; predictive modeling; programs; quantitative imaging; reconstruction; responders and non-responders; treatment trial; ventilation

PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD), which includes conditions such as emphysema, is the fourth leading cause of death in the United States. Although progression of COPD can be slowed through lifestyle changes, the disease is incurable. New treatment/management options emerged recently when the FDA approved endobronchial valves (EBV) as a Breakthrough Device for the treatment of severe COPD. EBVs provide a new treatment pathway but their implantation is associated with morbidity and mortality risks. In addition, not every patient who undergoes implantation has demonstrated the expected benefits from the device. Trial data indicate that imaging data can be used to help identify patients in whom an EBV will be effective, showing specifically that effectiveness increases in patients with heterogeneous COPD with complete fissures in the targeted lobes. The EBV is officially indicated for “severe emphysema in regions of the lung that have little to no collateral ventilation,” but interpretation of these criteria is imprecise and qualitative. The goal of this proposal is to develop more unified, robust, and quantitative criteria that will more successfully identify patients that are likely to respond to EBV implantation. The three most predictive variables identified by clinical trials to date have been the degree of severity of COPD, the heterogeneity of COPD between adjacent lobes and fissure completeness, all of which can be assessed by CT imaging. However, quantification of COPD severity can be confounded by the CT acquisition and reconstruction parameter selections used in the imaging protocol. These can also affect the ability to assess fissure completeness. In addition, traditional measures of COPD severity (i.e. Emphysema score) do not distinguish lung destruction from airtrapping, which may be important in assessing whether a patient is a good candidate for an EBV. In this proposal, we will investigate methods to standardize CT imaging data that would allow for more robust quantitative image analysis and apply the resulting methods to develop a more robust criteria for patient selection for the application of EBV devices. Therefore, our Specific Aims are: (1) To develop methods for robust, quantitative CT estimates of lung density and the Fissure Integrity Score (FIS). This will involve the application of methods to account for differences in CT scanners and acquisition/reconstruction parameters as well as machine learning methods to more accurately assess lung fissure integrity; (2) To develop and test an EBV predictive model using improved measures. Using the improved estimates of lung density and FIS from aim 1, we will develop and test a predictive model that distinguishes EBV responders from non-responders; and (3) To extend the EBV Prediction Model to incorporate additional biomarkers. We will extend the EBV scoring model by investigating other biomarkers that may provide complementary information, such as distinguishing airtrapping from COPD and an assessment of lung vascularity. We evaluate the ability of this extended model to improve our ability to distinguish EBV responders from non-responders.

项目摘要 慢性阻塞性肺疾病（COPD），包括肺气肿等疾病，是第四 美国的主要死亡原因。尽管COPD的进展可以通过生活方式减慢 变化，这种疾病是无法治愈的。 FDA最近出现了新的治疗/管理选项 批准的支气管阀（EBV）是用于治疗严重COPD的突破装置。 EBV 提供新的治疗途径，但它们的实施与发病率和死亡率风险有关。在 此外，并非每个接受实施的患者都证明了该设备的预期收益。 试验数据表明，成像数据可用于帮助识别EBV有效的患者， 具体表明，异质COPD患者的有效性增加了 在目标的爱中。 EBV正式指示“肺部地区的严重肺气肿几乎没有 没有附带通风，“但是对这些标准的解释是植物和定性的。 建议是制定更统一，健壮和定量标准，以更成功地确定 可能对EBV植入反应的患者。由 迄今为止的临床试验是COPD的严重程度，COPD的异质性 爱与裂缝完整性，所有这些都可以通过CT成像进行评估。但是，数量COPD CT采集和重建参数选择可能会混淆严重性 协议。这些也会影响评估裂缝完整性的能力。另外，传统的措施 COPD严重程度（即肺气肿得分）不会区分肺部破坏与AIRTRAPTION，这可能是 评估患者是否是EBV的良好候选者很重要。在此提案中，我们将调查 标准化CT成像数据的方法，该数据将允许更强大的定量图像分析并应用 生成的方法是为使用EBV设备的患者选择而制定更强大的标准。 因此，我们的具体目的是：（1）开发用于肺部稳健，定量CT估计值的方法 密度和裂痕完整性评分（FIS）。这将涉及应用方法来解释 CT扫描仪和采集/重建参数的差异以及机器学习方法 更准确地评估肺裂缝完整性； （2）使用 改进的措施。利用AIM 1的肺密度和FI的估计值的改进，我们将发展和 测试将EBV响应者与非反应者区分开的预测模型； （3）扩展EBV 预测模型以纳入其他生物标志物。我们将通过 调查可能提供完整信息的其他生物标志物，例如区分空运 来自COPD和肺血管的评估。我们评估了这种扩展模型改进的能力 我们将EBV响应者与非反应者区分开的能力。