# Quantitative Imaging Analysis to Identify Chronic Respiratory Disease

> **NIH VA I01** · VA BOSTON HEALTH CARE SYSTEM · 2024 · —

## Abstract

Chronic respiratory diseases (CRDs), such as chronic obstructive pulmonary disease (COPD) and
interstitial lung disease (ILD) are currently the 4th leading cause of death in the U.S., yet often remain
undiagnosed and under-treated until the advanced stages. Current research suggests an increased
prevalence and rising incidence of CRDs among Veterans relative to the general population. Yet, despite a
high prevalence and evidence supporting improved outcomes with early medical management, no screening
programs currently exist for CRDs. Chest computed tomography (CT), a medical imaging modality employed
for lung cancer screening (LCS), can detect structural changes in the lungs associated with CRDs, but their
use has been limited by (1) the labor-intensive nature and inter-person variability of visual interpretation of
images, (2) clinical reports which are often focused solely on acute findings (lung nodules, pneumonia) with
inconsistent reporting of chronic conditions. Quantitative imaging analysis (QIA) techniques have been
developed which can objectively detect and quantify a broad range of pathological changes directly from chest
CT imaging data, often with increased sensitivity relative to visual methods. We assert the application of QIA
to clinically obtained chest CT data within the auspices of well-organized LCS program represents an
opportunity to identify and characterize undiagnosed CRDs among a high-risk Veteran population.
 We propose to develop and validate a clinical tool, the Quantitative Imaging Analysis-based Risk
Summary (QIA-RS), which will translate imaging information from LCS chest CTs into practicable evidence
in three CRD domains: lung function impairment, symptoms and functional status, and future respiratory
healthcare utilization. QIA will be performed using TRM-approved software behind the VA firewall to assess
features of CRD (e.g. emphysema, airway wall thickness, interstitial lung abnormalities, and total lung
capacity) on archived and newly acquired chest CT data from patients enrolled in the VA Boston LCS program
(4,777 unique referrals between 2017-2019, with ~1400 new referrals/year). Clinically-ascertained spirometry
available in approximately 2,400 subjects, will be used to train and validate models to predict lung function
impairment using QIA features as predictors (QIA-RS lung function impairment domain – Aim 1). Because
individuals with undiagnosed CRDs (the target population for our QIA-RS tool) have been incompletely
characterized in the literature, we propose to recruit individuals with no previous history of lung disease at the
time of LCS (n=300) for an in-person study visit where lung function, respiratory symptoms, and functional
status (exercise capacity, health related quality of life) will be assessed and used to identify thresholds of QIA-
assessed features associated with impairments (Aim 2 – QIA-RS respiratory symptom and functional status
domain). We will follow individuals recruited in Aim 2 ...

## Key facts

- **NIH application ID:** 10812303
- **Project number:** 5I01CX002193-03
- **Recipient organization:** VA BOSTON HEALTH CARE SYSTEM
- **Principal Investigator:** Emily S Wan
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2022-01-01 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10812303

## Citation

> US National Institutes of Health, RePORTER application 10812303, Quantitative Imaging Analysis to Identify Chronic Respiratory Disease (5I01CX002193-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10812303. Licensed CC0.

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