Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in the US, with nearly 60 million confirmed cases world-wide. Emphysema and small airways disease (SAD) are two main components of COPD. Emphysema, associated with severe COPD, results in alveolar destruction and is a leading cause of mortality in this population. Considered at the tissue level an end-stage disease, there are limited treatment options once diagnosed. As such, there is a clear unmet clinical need to identify markers that predict the early onset of emphysema. Small airways disease, a treatable component of COPD, has been identified in recent studies as a potential precursor of emphysema. Although promising, clinical techniques were unable to measure SAD hindering its use as a marker. This limitation was overcome through our development of a 3D analytical technique called Parametric Response Mapping (PRM). When applied to computed tomography (CT) scans, PRM identifies non-emphysematous air trapping, an indirect measure of SAD, even in the presence of emphysema. In fact, we have demonstrated that the relative lung volume of PRM-derived functional SAD (%PRMfSAD) predicted spirometric decline in COPD patients, highlighting its potential as an indicator of COPD progression. Nevertheless, %PRMfSAD provides only a whole-lung assessment, limiting its potential at detecting the onset and local progression of emphysema. To fully realize PRMfSAD as a predictor of emphysema, we have advanced our PRM technique by applying topological methods, based on the Minkowski Functionals, to our 3D PRM classification maps. Referred to as topological PRM (tPRM), this method reported in Scientific Reports extracts and quantifies features from 3D PRMfSAD resulting in 3D maps of relative density (V; analogous to %PRMfSAD), surface area (S), mean breadth (B) and Euler-Poincar statistic (?). Our preliminary results have shown that tPRMfSAD correlated better to clinical measures than the %PRMfSAD. Based on these findings, we hypothesize that our approach will improve PRM sensitivity to disease progression while providing the spatial information needed to detect the onset of emphysema. We have set out three aims to test our hypothesis.
In Aim 1, we will evaluate the sensitivity of our tPRM method to CT signal variability associated with scanner type, CT acquisition and reconstruction kernel, as well as corroborate correlations in tPRM to various clinical measures as previously reported.
In Aim 2, we will determine distinctions in tPRM to contributions of emphysema and SAD as measured from microCT analysis of frozen explanted lung cores obtained from lung transplant recipients with end-stage COPD. Finally, in Aim 3, we will correlate longitudinal changes over a 5-yr interval in tPRM to clinically- relevant measures.
These aims will be accomplished through support from the NIH-sponsored clinical trial COPDGene and continued multi-center and multi-disciplinary collaborations. Relevance: We anticipate that the successful outcome of this effort will improve the diagnostic capability of CT imaging through the realization of an early marker for the onset of emphysema, leading to improved patient care through precision medicine.

Public Health Relevance

Emphysema is a leading cause of morbidity and mortality in patients diagnosed with chronic obstructive pulmonary disease (COPD). Resulting in irreversible alveolar destruction, few treatment options are available for patients afflicted with emphysema. As such there is a clear need to identify markers that predict its onset. Our proposal will evaluate our advanced CT-based analytical technique, called Topological Parametric Reponses Mapping, for improved quantification of small airways disease, identified as a precursor of emphysema. Through rigorous testing and evaluation, we will establish the foundation for the future application of our approach as a clinically relevant early imaging readout of emphysema.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Medical Imaging Study Section (MEDI)
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Postow, Lisa
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University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
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