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

项目总结 包括肺气肿在内的慢性阻塞性肺疾病(COPD)是第四种 是美国最大的死因。尽管慢性阻塞性肺疾病的进展可以通过生活方式来减缓 变化，这种病是不治之症。最近出现了新的治疗/管理选择，因为FDA 批准的支气管内瓣膜(EBV)作为治疗重度COPD的突破性装置。EBV 提供了一种新的治疗途径，但它们的植入与发病率和死亡风险有关。在……里面 此外，并不是每个接受植入的患者都表现出了该设备的预期好处。 试验数据表明，成像数据可以用来帮助识别EBV将有效的患者， 具体地说，在完全裂隙的异质性COPD患者中，有效性增加 在目标脑叶中。EBV的官方指征是“严重的肺气肿，出现在肺脏中 但对这些标准的解释是不准确和定性的。这样做的目的是 建议是制定更统一、更强大和更量化的标准，以便更成功地确定 可能对EBV植入有反应的患者。由确定的三个最具预测性的变量 到目前为止的临床试验一直是COPD的严重程度，COPD在相邻的 肺叶和裂隙的完整性，所有这些都可以通过CT成像进行评估。然而，COPD的量化 严重程度可以通过在成像中使用的CT采集和重建参数选择来混淆 协议。这些也会影响评估裂缝完整性的能力。此外，传统的措施包括 慢性阻塞性肺疾病的严重程度(即肺气肿评分)不能区分肺破坏和空气滞留，后者可能是 在评估患者是否是EBV的好候选人时很重要。在这份提案中，我们将调查 标准化CT成像数据的方法，以实现更可靠的定量图像分析和应用 由此产生的方法为EBV设备的应用制定了更可靠的患者选择标准。 因此，我们的具体目标是：(1)开发稳健的、定量的肺部CT评估方法 密度和裂隙完整性评分(FIS)。这将涉及到应用方法来解释 CT扫描仪和采集/重建参数以及机器学习方法的差异 更准确地评估肺裂完整性；(2)开发和测试EBV预测模型 改进措施。使用来自目标1的改进的肺密度和FIS估计，我们将开发和 测试区分EBV应答者和非应答者的预测模型；以及(3)扩展EBV 包含其他生物标志物的预测模型。我们将通过以下方式扩展EBV评分模型 调查其他可能提供补充信息的生物标志物，例如区分空气滞留 慢性阻塞性肺疾病和肺血管的评估。我们评估了这个扩展模型改进的能力 我们区分EBV应答者和非应答者的能力。

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.