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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $687,505

## Abstract

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 distin...

## Key facts

- **NIH application ID:** 10212136
- **Project number:** 1R01HL153964-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** MATTHEW S BROWN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $687,505
- **Award type:** 1
- **Project period:** 2021-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212136

## Citation

> US National Institutes of Health, RePORTER application 10212136, Robust Clinical Translation of CT Imaging Biomarker in COPD for EBV Patient Selection (1R01HL153964-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10212136. Licensed CC0.

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