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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

基本信息

批准号：
10593063
负责人：
MATTHEW S BROWN
金额：
$ 68.23万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
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：
2021-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10593063
关键词：
Accounting Affect Air Biological Markers Blood Vessels Breakthrough device 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 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 Qualifying 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 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

项目摘要

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.

项目总结