# An Integrative Radiogenomic Approach to Design Genetically-Informed Image Biomarker for Characterizing COPD

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $478,218

## Abstract

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Abstract
 Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of death worldwide with a
devastating socio-economic burden impacting more than three million individuals per year in the US. The
primary environmental risk factor in the susceptible population is smoking, which causes an exaggerated
inflammatory response. However, many factors including several genetic risk variants substantially influence
the susceptibility. Twin-based studies show that families with emphysema have a higher risk for the disease.
The two different major phenotypes of COPD are small airway remodeling (airway disease) and alveolar
destruction (emphysema). Although these two major phenotypes result in a similar deficiency in global lung
function, the relationship between them is complicated and likely involves feedback mechanisms. Developing
an objective method to characterize lung phenotypes is critical since treatment candidates vary based on
phenotype. Measurements from High-Resolution Computed Tomography (HRCT) images are increasingly
used to describe COPD since they can quantitatively describe the contribution of the phenotypes. To discover
the genetic risk variants, Genome-Association Studies (GWAS) have focused on either the physiological lung
function or a simple threshold-based measurement from lung CT, neither of which fully characterizes
phenotypic subtypes or the distribution pattern of disease. The proposed studies will take advantage of the rich
image and genetic data jointly to build a genetically-informed imaging biomarker to characterize each patient.
For each patient, our method summarizes the CT image to a vector representation that accurately describes
the severity of the disease. Also, a method to link the representation back to the genetic risk variants will be
developed. If successful, these methods can be used to monitor the efficacy of treatment or progression of the
disease using imaging data. Successful execution of the second aim will result in better understanding of the
etiology of different disease subtypes and discovery of novel genetic pathways that could be used as potential
drug targets. Furthermore, the patient representation enables the use of image data to construct a more
powerful model to predict the so-called acute exacerbation event. Predicting the exacerbations is clinically
important since they cause further damage to the lung.
 In Aim 1, we develop and implement a novel image biomarker that is mutually informed by imaging and
genetic data from each patient. Our statistical method in Aim 2 elucidates the underlying genetic pathways
behind the abnormal anatomical variations explained by the biomarker. We validate our method on data from
10,300 patients in the COPDGene dataset.
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## Key facts

- **NIH application ID:** 10866646
- **Project number:** 7R01HL141813-06
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Kayhan Batmanghelich
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $478,218
- **Award type:** 7
- **Project period:** 2018-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866646

## Citation

> US National Institutes of Health, RePORTER application 10866646, An Integrative Radiogenomic Approach to Design Genetically-Informed Image Biomarker for Characterizing COPD (7R01HL141813-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10866646. Licensed CC0.

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