Our goal is to understand how thoracic multi detector-row computed tomography (MDCT) combined with innovative image analysis techniques can be used to identify COPD patients at greatest risk. The overall objective of this proposal is to focus on the pulmonary vasculature. We hypothesize that novel quantitative MDCT-based metrics that characterize heterogeneity of pulmonary parenchymal perfusion and pulmonary vascular anatomy in early stage COPD subjects will allow us to identify subjects at greater risk for rapid disease progression. Numerous observations from our own laboratories and others point to an abnormal pulmonary vascular response to smoking induced parenchymal inflammation that leads to the development of emphysema. We have established imaging methods for the quantitative assessment of the pulmonary vascular tree and regional pulmonary parenchymal perfusion status. The former measure takes advantage of volumetric MDCT and the later makes use of dual energy MDCT (DE-MDCT) to provide clinically implementable tools for the interrogation of lung structure and function.
Our Specific Aims are as follows: (1) determine whether objective, quantitative measures of increased PBV heterogeneity derived from DE-MDCT, as an index of emphysema susceptibility, will serve as a very early indicator of rapid emphysema progression (assessed by MDCT) and lung function decline; (2) determine whether anatomic changes in pulmonary vascular geometry will identify subjects at risk for more rapid emphysema progression and lung function decline, possibly in more urgent need of intervention; and (3) determine whether early anatomic and functional markers of vascular disease will combine to identify subjects at increased risk for development of frequent exacerbations who require more intense therapy before significant lung function decline has occurred. In this time-sensitive proposal, we will leverage the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). SPIROMICS offers a unique opportunity to study at risk smokers and individuals with COPD in a longitudinal study that are well-characterized in terms of repeated clinical, physiological and biological evaluations. We propose to insert a baseline DE-MDCT scan to evaluate an index of pulmonary perfusion heterogeneity (Perfused Blood Volume: PBV) in 200 GOLD 0/I subjects at baseline and to insert a 3rd year MDCT exam into the SPIROMICS protocol for these same individuals. We also propose a year 3 MDCT exam in an additional 125 GOLD II-III SPIROMICS subjects. Our over-riding goal is to evaluate early functional and anatomic indices of vascular dysfunction to determine the association between these metrics and rate of emphysema progression, lung function decline and risk of AECOPD.

Public Health Relevance

We seek to evaluate the role of imaging-based measures of pulmonary vascular structure and function to identify smokers at risk of rapid emphysema progression and/or at risk of having frequent acute exacerbations of their chronic obstructive pulmonary disease. We propose to utilize an existing NIH funded cohort (SPIROMICS) as the setting for this study. Novel imaging- based measures of vascular status which predict rapid disease progression and likely exacerbation will provide the tools necessary for development of new treatments which can be targeted to, tested in and deployed in appropriate subsets of the smoking population.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL126838-02
Application #
9054918
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Punturieri, Antonello
Project Start
2015-04-15
Project End
2019-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Iowa
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52246
Labaki, Wassim W; Xia, Meng; Murray, Susan et al. (2018) NT-proBNP in stable COPD and future exacerbation risk: Analysis of the SPIROMICS cohort. Respir Med 140:87-93
Labaki, Wassim W; Han, MeiLan K (2018) Artificial Intelligence and Chest Imaging. Will Deep Learning Make Us Smarter? Am J Respir Crit Care Med 197:148-150
Han, MeiLan K; Quibrera, Pedro M; Carretta, Elizabeth E et al. (2017) Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort. Lancet Respir Med 5:619-626
Labaki, Wassim W; Martinez, Carlos H; Martinez, Fernando J et al. (2017) The Role of Chest Computed Tomography in the Evaluation and Management of the Patient with Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 196:1372-1379
Keene, Jason D; Jacobson, Sean; Kechris, Katerina et al. (2017) Biomarkers Predictive of Exacerbations in the SPIROMICS and COPDGene Cohorts. Am J Respir Crit Care Med 195:473-481
Saha, Punam K; Basu, Subhadip; Hoffman, Eric A (2016) Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications. IEEE Trans Fuzzy Syst 24:1121-1133
Hoffman, Eric A; Lynch, David A; Barr, R Graham et al. (2016) Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes. J Magn Reson Imaging 43:544-57
Jin, Dakai; Guo, Junfeng; Dougherty, Timothy M et al. (2016) A semi-automatic framework of measuring pulmonary arterial metrics at anatomic airway locations using CT imaging. Proc SPIE Int Soc Opt Eng 9788:
Jin, Dakai; Iyer, Krishna S; Chen, Cheng et al. (2016) A Robust and Efficient Curve Skeletonization Algorithm for Tree-Like Objects Using Minimum Cost Paths. Pattern Recognit Lett 76:32-40