Smoking related pulmonary vascular disease has been demonstrated to be an independent predictor of morbidity and mortality in patients with COPD. Previous investigation and observation suggests that there are several types of pathologic pulmonary vascular remodeling possible in smokers. These range from inflammatory remodeling with progressive luminal occlusion, aberrant vessel elongation in regions of hyperinflation, and outright loss of vasculature in regions of severe emphysematous destruction. While there are existing tools that may be used to investigate the aggregate effect of these processes such as right heart catheterization, echocardiography, and measurements of diffusing capacity for carbon monoxide (DLCO), none can differentiate the types of vascular remodeling/deformation present nor their relative contribution to clinical impairment. The purpose of this investigation is to improve understanding of the clinical impact and epidemiologic associations of pulmonary vascular remodeling in smokers. We will do this by performing CT based quantitative measures of the mediastinal (Aim 1) and intra-parenchymal vasculature (Aim 2) in all subjects enrolled in the COPDGene Study and then clinically validating these measures with echocardiography, cardiac MRI and measures of DLCO (Aim 3). In the final steps of Aim 3 we will examine the overlap and associations between pulmonary and cardiovascular disease (both clinical diagnosed CVD and both coronary and thoracic aortic calcification) and the relationship between pulmonary vascular morphology and exercise capacity, acute exacerbations of COPD, symptoms, and mortality. We believe that this will lead to improved understanding of the pathophysiology of COPD and may ultimately improve the care of patients with COPD.
Computed tomographic assessments of pulmonary vascular morphology in subjects enrolled in the COPDGene Study may provide insight into the burden, severity, and clinical impact of pulmonary vascular remodeling in smokers. A deeper understanding of the types of remodeling present (narrowing, elongation, drop out) may lead to new therapies for patients with COPD.
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| González, Germán; Washko, George R; Estépar, Raúl San José (2018) Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans. Proc SPIE Int Soc Opt Eng 10574: |
| Cano-Espinosa, Carlos; González, Germán; Washko, George R et al. (2018) Automated Agatston Score Computation in non-ECG Gated CT Scans Using Deep Learning. Proc SPIE Int Soc Opt Eng 10574: |
| Vegas-Sánchez-Ferrero, Gonzalo; Ledesma-Carbayo, Maria J; Washko, George R et al. (2018) Autocalibration method for non-stationary CT bias correction. Med Image Anal 44:115-125 |
| Campo, Mónica Iturrioz; Pascau, Javier; José Estépar, Raúl San (2018) EMPHYSEMA QUANTIFICATION ON SIMULATED X-RAYS THROUGH DEEP LEARNING TECHNIQUES. Proc IEEE Int Symp Biomed Imaging 2018:273-276 |
| Nardelli, Pietro; Jimenez-Carretero, Daniel; Bermejo-Pelaez, David et al. (2018) Pulmonary Artery-Vein Classification in CT Images Using Deep Learning. IEEE Trans Med Imaging 37:2428-2440 |
| Ramos-Llorden, Gabriel; Vegas-Sanchez-Ferrero, Gonzalo; Bjork, Marcus et al. (2018) NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI Mapping. IEEE Trans Med Imaging 37:2414-2427 |
| Diaz, Alejandro A; Strand, Matthew; Coxson, Harvey O et al. (2018) Disease Severity Dependence of the Longitudinal Association Between CT Lung Density and Lung Function in Smokers. Chest 153:638-645 |
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