Computerized tomography in chronic obstructive pulmonary disease (COPD) has been the subject of intense interest in the research and clinical community. Methods have been developed to objectively detect and quantify processes affecting the lung parenchyma, airways and vasculature, as well as extrapulmonary manifestations of the noxious effects of chronic inhalational exposures, such as tobacco smoke. This article provides a brief overview of image-based advances in COPD research and then discusses how these advances have translated to clinical care, finishing with a brief description of a path forward for the convergence of research and care at the bedside.
Computed tomography provides information relevant to parenchymal, airway, vascular, and extrapulmonary manifestations of chronic obstructive pulmonary disease (COPD).
Currently, the primary clinical application of imaging in COPD is to evaluate emphysema in candidates for surgical or bronchoscopic lung volume reduction.
At the time of diagnosis and in management of COPD, imaging is also utilized to look for alternative or additional causes of dyspnea and cough.
There has been a lag between research investigation and clinical applications of imaging in COPD; however, promising studies are ongoing with near-term potential to impact disease diagnosis, prognosis, and therapy.
Computed tomography (CT) has been extensively leveraged in research investigations of chronic obstructive pulmonary disease (COPD). Computer-aided tools can provide objective characterization of lung disease, yet advances in clinical care lag behind discoveries made in research. Although this phenomenon is generally true of all medical conditions, the gulf between imaging-based research and COPD treatment is striking. This article provides an overview of the advances made in COPD-based image processing over the past 4 decades, describes the current standard of practice for COPD care, and then provides a brief description of the path forward where the two may meet.
CT was introduced into clinical care in the 1980s, and since that time there have been extensive efforts to identify and quantify features that may be used to improve understanding of chronic respiratory conditions such as COPD. One of the pathologic bases for COPD is emphysematous destruction of the lung parenchyma. This process, defined as abnormal and permanent dilation of the distal airspaces due to destruction of alveolar walls, results in reduced lung elastic recoil leading to hyperinflation, expiratory airflow obstruction, and breathlessness. On CT, emphysema appears as holes in the lung that can be readily quantified by measuring the density or attenuation (in Hounsfield units, HU) of the parenchyma. While normal lung tissue may have an attenuation of approximately −856 HU, emphysema has attenuation values less than −910 to −960 HU depending on the parameters used for image acquisition and reconstruction. Thus, an HU threshold can be selected that differentiates nonemphysematous and emphysematous lung, and the percent of low attenuation areas (LAA%: volume of low attenuating lung/total lung volume ∗ 100) can be calculated as the proportion of emphysematous lung tissue ( Fig. 1 ). A second way to objectively quantify emphysema is by calculating the HU value that demarcates the lowest 15% of the lung histogram from the remaining 85%, called the percent density (PD) 15. Both of these measures from the density histogram are obtained at suspended full inspiration, and a limitation of the test is suboptimal inspiration during the CT scan. One way to overcome this limitation is to adjust PD15 for differences in lung volume, although as hyperinflation is part of the disease process in COPD, it is possible this may cause some overcorrection in some cases.
The initial efforts to objectify emphysema were performed with density mask or densitometric analyses, which demonstrated that there was a direct relationship between the degree of emphysema evident on CT and both the severity of lung disease and histopathologically determined degree of airspace dilation. , Multiple subsequent investigations have replicated these findings, and densitometry has become the standard method for quantifying airspace dilation in clinical, epidemiologic, genetic, and therapeutic investigation. The evolution of computational capacity has fostered the growth of more advanced postprocessing machine learning and deep learning techniques. These algorithms have several advantages over conventional densitometry, as they incorporate information on the distribution of emphysema within the lung and quantifying the admixture of centrilobular, paraseptal, and panlobular emphysema present in an individual.
CT-based investigation has also demonstrated that the lung manifests divergent responses to noxious insults such as chronic tobacco smoke exposure. Smokers may appear resilient to the injurious effects of tobacco smoke or may develop emphysema and even pulmonary fibrosis. Recent investigation suggests approximately 6% to 8% of smokers over the age of 50 have some degree of interstitial remodeling of the lung parenchyma. These processes have collectively been termed interstitial lung abnormalities (ILAs) and have been shown to have similar genetic associations as advanced pulmonary fibrosis. The presence of ILA is independently associated with all-cause and respiratory-specific mortality in population-based studies. Extensive work is ongoing to determine which subset of these ILAs progress to classic interstitial lung disease and potentially when to initiate antifibrotic therapy.
A second area of focused investigation in smokers is the CT-based assessment of airway disease. Studies using these techniques initially reported that smokers with thicker airway walls on CT scan were more likely to have more compromised lung function. , Histopathologic studies have repeatedly demonstrated that remodeling of the small airways is the primary event in the development of expiatory airflow obstruction in smokers. , Using retrograde catheterization and explanted human lungs, Hogg and colleagues demonstrated that those airways less than 2 mm in diameter were the site of greatest resistance to airflow in COPD. Subsequent work has explored the pathologic changes to the airways and discovered that not only does the remodeling process manifest as inflammation, fibrosis, and mural plugging, but these processes may culminate in destruction and ultimately an absence of these small airways. More recent work has demonstrated that the reduction in numbers of terminal bronchioles was highly correlated with disease stage but was also associated with reductions of the more proximal airway count collected from clinical CT scanning, suggesting that objective assessments of the airways on conventional CT may provide insight into the distal lung.
Beyond lung parenchyma and airways
Another quantitative method for assessing the airways on CT is parametric response mapping (PRM). This technique utilizes the difference in lung density between inspiratory and expiratory scans to calculate gas trapping caused by small airway disease. Small airway obstruction does not allow full deflation of the lung parenchyma; this results in a lower attenuation of these areas on expiration when compared with lung parenchyma supplied by normal airways. PRM has been used extensively in the clinical characterization of COPD and has been recently validated against micro-CT based assessments of distal lung architecture.
The increasing utilization of imaging in COPD and the multidisciplinary approaches to research have accelerated the refinement of techniques focused on the parenchyma and airways while facilitating the collection of imaging data that have provided more full characterization of the breadth of thoracic pathology evident in smokers. Examples of this work include morphologic assessments of the pulmonary vasculature, both the intraparenchymal vessels and central pulmonary artery and aorta. Vascular measures, including arterial and venous segmentation on CT, can be used to identify people at greatest risk for disease progression, improve the understanding of the interdependence of heart and lung, and be used to predict acute respiratory exacerbations and response to therapeutic interventions such as bronchoscopic lung volume reduction ( Fig. 2 ).