Secondary Logo

Journal Logo

Review Articles

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease

Li, Tao1,2; Zhou, Hao-Peng3; Zhou, Zhi-Jun4; Guo, Li-Quan5; Zhou, Linfu1,6

Editor(s): Wei, Pei-Fang

Author Information
doi: 10.1097/CM9.0000000000001724
  • Open

Abstract

Introduction

Chronic obstructive pulmonary disease (COPD) is a common and frequently occurring disease of the respiratory system. Its primary clinical features are persistent airflow limitations, persistent respiratory disease symptoms, high morbidity, disability, and mortality, all of which can seriously affect the quality of life in patients with COPD;[1] additionally, it is also one of the leading causes of death worldwide.[2] The diagnosis and treatment of COPD are rapidly changing; hence, a better understanding of recent advances is important for delivering optimal patient care.[3]

Spirometry is the reference gold standard for diagnosing COPD and assessing disease severity. Spirometry-defined and -diagnosed COPD is highly prevalent in the adult population of China. Cigarette smoking, ambient air pollution, being underweight, previous childhood chronic cough, lower levels of education, and a parental history of respiratory diseases are the major risk factors for COPD.[4] These can often result in the irreversible destruction of lung tissue (emphysema) and inflammation, remodeling of small airways, or both. Although COPD is diagnosed and staged with a pulmonary function test (PFT), computed tomography (CT) enables quantification of abnormal changes in the lung parenchyma.[5]

COPD is often caused by continuous inhalation of toxic particles and gases, with most patients displaying a history of tobacco inhalation.[6] Recent research indicated that among individuals aged < 50 years with ≥10 pack-years of tobacco consumption, 15% fulfil the criteria for early COPD. Individuals with early COPD more often have chronic respiratory symptoms and lung function impairment; this is alongside an increased risk of acute exacerbation, hospitalizations, and early death.[7]

Continuous damage in the lungs recruits local inflammatory immune cells, resulting in the formation of abnormal scar tissue that affects the morphology of lung tissue.[8] This damage, in turn, can alter the respiratory function of the lungs to varying degrees. In different parts of the lung tissue, the immune response can be inconsistent, resulting in varying degrees of damage.[9] The main disease manifestations are chronic bronchial inflammation, enlargement of the bronchial mucous glands, and goblet metaplasia of the airway endothelium.[10]

Insufficiency of PFT in Assessing COPD Severity

The PFT, a convenient manipulatory technique, provides direct measurement for monitoring the progression and severity of COPD.[11] Different individuals have different pathophysiological processes. Significant heterogeneity exists in patients with COPD regarding clinical manifestations and disease progression.[12] However, accumulating studies have shown that lung function results are similar in patients with varying degrees of COPD. While there are differences in clinical symptoms and test indicators, PFT cannot accurately explain an individual patient's situation.

Lung function decreases progressively in COPD. To some extent, the forced expiratory volume in 1 s (FEV1) can reflect both the severity and complexity of lung function. However, in actuality, FEV1 alone cannot address the severity and complexity of the disease. Hence, FEV1 cannot be used as an ideal indicator for the diagnosis, evaluation, and treatment of COPD.[13] A mean forced expiratory flow between 25% and 75% (FEF25-75) is considered as the most sensitive measurement of airflow in peripheral airways representative of airflow obstruction, which is reduced in early bronchial impairment in association with small airway disease (SAD).[14] Of note, FEF25-75 also includes large airway flow. Imaging can directly display the structure of lung tissue, thus characterizing COPD and reflecting different states of lung tissue among different individuals. As treatments have become more personalized, imaging may be a unique way to understand what COPD actually looks like [Figure 1].

Figure 1
Figure 1:
Coronal CT images of three patients with varying degrees of small airway disease. (A) A 64-year-old male with 56 pack-years, FEV1 96% predicted, FEV1/FVC 102%, RV/TLC 95% predicted, and DLCO 95% predicted. This patient showed normal lung function with air trapping. (B) A 69-year-old male with 72 pack-years, FEV1 90% predicted, FEV1/FVC 85%, RV/TLC 117% predicted, and DLCO 75% predicted. This patient was diagnosed with chronic bronchitis, emphysema, and bullae, accompanied by evident air trapping. (C) A 70-year-old male with 35 pack-years and COPD GOLD stage 3, FEV1 60% predicted, FEV1/FVC 58%, RV/TLC 166% predicted, and DLCO 85% predicted. This patient showed predominantly functional small airway disease in both lungs. COPD: Chronic obstructive pulmonary disease; CT: Computed tomography; DLCO: Diffusing capacity of the lung for carbon monoxide; FEV1: Forced expiratory volume in 1 s; FVC: Forced vital capacity; GOLD: Global Initiative for Chronic Obstructive Lung Disease; RV: Residual volume; TLC: Total lung capacity.

Anatomical Features and Physiological Characteristics of Small Airways

Bronchioles are designated as bronchial tubes lacking mucous glands and cartilage. Bronchioles account for the majority of small airways. Clinically, small airways are usually located in the eighth grade respiratory bronchus, which comprises almost all (98.8%, approximately 4500 mL) of the total lung volume.[15] The inner diameter of the small airway lumen is less than 2 mm. The minimum diameter can reach approximately 0.06 mm after continuous branching.[16]

Since small airways have no cartilaginous support, they are more easily affected by pressure changes in the thoracic cavity.[17] Approximately 80% of respiratory resistance in the lungs comes from large airways, whereas only 20% of resistance comes from small airways.[18] Following the combined action of both neural and humoral regulation, small airway smooth muscles can relax and contract, altering the airway diameter, and subsequently controlling gas exchange.

Pathological Changes in Small Airways in COPD

SAD, a cardinal feature of COPD, was first recognized in the 19th century. Airway epithelial cells are the first barrier to harmful gases and particles, which are present in the small airways. After exposure to harmful substances, the local inflammatory reaction is upregulated, thereby inducing morphological changes in airways [Figure 2].[19]

Figure 2
Figure 2:
Main mechanism of small airway disease onset and progression of chronic obstructive pulmonary disease (COPD).

COPD is caused by continuous inhalation of toxic particles, gases, and pathogens that induce respiratory tract infections; however, most cases are related to tobacco inhalation. Small airways are narrowed as the airway wall is thickened as a result of inflammation. In response, aberrant repair processes result in distal hypoventilation, air entrapment, and low blood flow. In addition, the lumen is occupied by tenacious mucus as a result of goblet hyperplasia, mucus hypersecretion, and impaired mucociliary clearance. These contribute to the onset and progression of small airways disease. This can affect the respiratory function of the lungs to varying degrees and can reduce lung cross-sectional area, thus increasing the risk of lung failure. CT images can demonstrate different signs: limited ground glass, shadow of center nodule with blurred edges, tree-in-bud sign, local density reduction area or mosaic perfusion, etc.

The diverse histopathological features associated with SAD underpin the heterogeneous nature of COPD. Previously, studies have described small airway abnormalities in COPD as inflammation, fibrosis, and mucus blockages, which are related to the severity of airflow obstruction. Hogg et al[20] discovered that the thickening of airway walls was closely related to the repair of inner wall damage. Moreover, the degree of lumen obstruction and inflammatory response were weakly correlated with the progression and prognosis of COPD.

McDonough et al[21] selected multi-slice spiral CT images of 78 patients with COPD, in which the bronchus structure can be clearly displayed, and judged by scoring on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) scale. The comparative results showed fewer small airways in GOLD stage 1 patients, which were further reduced in GOLD stage 3 and GOLD stage 4 patients. In fact, during the comparison process, researchers raised doubts regarding the reduced number of small airways, as they were unsure whether these small airways had narrowed without detection or even disappeared.

Current data suggest that the inflammatory response in small airways might precede fibrosis and remodel the lung tissue; furthermore, it may result in a clinically detectable decline in lung function.[22–24] Bhatt et al[25] detected pulmonary inflammatory responses in smokers and non-smokers, showing increased CD8+ T lymphocytes in the large airways, small airways, and parenchyma of smokers. Meanwhile, these CD8+ T lymphocytes induced apoptosis and necrosis of epithelial and endothelial cells in the lungs; however, the number of inflammatory cells was unrelated to the intensity of smoking.

Overview of COPD phenotypes

Typically, phenotypes are specific appearances or components of an object in a lifetime, influenced primarily by both the host's genotype and environment. Phenotyping is designated to identify patients with unique prognostic or therapeutic characteristics. In the research fields of COPD, identifying phenotypes, including symptoms, imaging, physiology, and molecular biology, has become an important venture.[26] Phenotypes facilitate a better understanding of disease evolution, punctuated by exacerbations; moreover, they can aid in favoring a series of potential targets for clinical translation.[27,28] With in-depth interpretation of COPD phenotypes, heterogeneity will be deciphered, bringing about new strategies for COPD diagnosis and treatment.

CT-identified Phenotypes of Small Airway Obstructions in Patients with COPD

Data analysis, based on chest CT scans, can quantify airway wall changes and emphysema severity, thus dividing patients with COPD into three main types, including those with symptoms of emphysema, airway changes, or both. After these divisions, they can then be further divided into different sub-types.[29] The major sites of obstruction lie within the small conducting airways in COPD. CT is currently the preferred method for obtaining lung morphology images in patients with COPD.[30] As a minimally invasive imaging technique, CT scanning is capable of providing high-contrast and high-resolution details of the lungs and airways.

Chest CT morphological manifestations allow the thickening of airway walls, emphysema, and trapped air in patients with COPD to be detected and measured in clinic.[31] Notably, the CT emphysema phenotype study can be conducted to explore the pathogenesis of COPD. Collectively, these associations between different sub-types and specific clinical manifestations support the definition of COPD phenotypes.[32]

Upon comparing the CT scans of small airways at different stages of COPD, it was found that stenosis and occlusion of the small airway occur earlier than emphysema or the gradual increase in peripheral airway resistance. This suggests that CT examinations can detect abnormalities before the development of clinical symptoms and earlier than lung function indexes.[33]

Small airways are usually visible at the site of inflammation, accompanied by exudation. SAD can be seen indirectly through air retention when there is no obvious emphysema or bronchiectasis.[34] Due to distal hypoventilation, air retention, and low blood flow, a fixed obstruction of a small airway leads to patchy differences in density.[35,36] During a normal expiratory period, the cross-sectional area of the lung decreases with increased lung attenuation value; here, trapped air is defined as the loss of the reduced cross-sectional area of the lung and increased lung attenuation value.

Based on available data, we summarized the possible CT phenotypes as functional resolution, trapped gas, parametric response mapping, ventilatory function, and micro-CT. Table 1 lists the visually defined CT phenotypes observed during small airway obstructions in patient with COPD. Moreover, Table 2 summarizes the available researches on CT phenotypes in recent years.

Table 1 - Main CT small airway phenotypes in COPD.
Phenotype Target Parameters Objectives
Functional phenotypes of HRCT Emphysema LAA-950 HU Quantify the degree of emphysema
Gas trapping LAA-856 or LAA-850 HU Investigate the association between gas trapping and lung function decline and RV
Thickness of airway wall or intraluminal area Wall area%Pi10 Investigate the association between thickness of airway wall and acute exacerbation rate and the severity of airflow obstruction
Parametric response mapping Emphysema PRMemph Distinguish the relative contributions of fSAD and emphysema
Gas trapping PRMfSAD Detect terminal bronchioles loss, luminal narrowing, and obstruction
Ventilatory function phenotype Ventilation function and emphysema Xenon gas ventilation map MDIs Determine the etiology of airflow limitation
Micro-CT phenotype Total number of terminal bronchioles and emphysema Preterminal bronchiolar length, wall thickness, lumen circularity, and number of alveoli Detect loss of terminal bronchioles early in small airway
COPD: Chronic obstructive pulmonary disease; CT: Computed tomography; fSAD: Functional small airways disease; HRCT: High resolution computed tomography; HU: Hounsfield unit; LAA: Low attenuation area; LAA-950: Low attenuation lung area below −950 Hounsfield units; LAA-856: Low attenuation lung area below −856 Hounsfield units; LAA-850: Low attenuation lung area below −850 Hounsfield units; MDIs: Material decomposition images; Pi10: Internal perimeter of 10 mm; PRM: Parametric response mapping; PRMemph: Emphysema on parametric response mapping; PRMfSAD: Functional small airways disease on parametric response mapping; RV: Residual volume.

Table 2 - Summary of researches on CT phenotypes of COPD in recent years.
Publication year First author Journal Method Main CT findings No. of patients Screened population Target
2019 Charbonnier et al Respir Med Thin section thoracic CT Pi10 is a clinically relevant biomarker of smoking-related airway injury in smokers with and without COPD 2046 Smokers with and without COPD Airway wall thickening and smoking status
2020 Pompe et al Radiology Inspiratory and expiratory volumetric thoracic CT FEV1 accounted for less than 10% of emphysema progression and less than 50% of air trapping progression detected at CT 4211 Smokers with and without COPD Emphysema and air trapping
2020 Liu et al Chin Med J Thoracic CT Airway IgA concentrations in mild and moderate COPD patients are directly associated with the severity of COPD with “emphysema phenotype” preceding severe airway limitation 30 COPD with “emphysema phenotype” Small airway and emphysema
2016 Subramanian et al Eur Respir J QCT The group with mixed pathology had upper lobe predominant emphysema. Patients with mild disease had better spirometric measures than the other phenotypes 441 COPD subjects (GOLD 0–3) Emphysema and airway disease
2020 Oh et al Radiology Inspiratory and expiratory CT Participants with visual emphysema demonstrated progressive airflow obstruction with lower values of FEV1/FVC and greater progression in quantitative emphysema measured by 15th percentile lung density 4095 GOLD 0 and participants with visually evident emphysema Visually evident emphysema
2016 Hartley et al J Allergy Clin Immunol QCT Air trapping measured based on expiratory/inspiratory ratio of mean lung density was significantly increased in patients with COPD and asthmatic patients 301 Smokers with COPD and asthma patients Air trapping and emphysema
2019 Arjomandi et al Eur Respir J QCT Subjects in higher RVCT/TLCCT tertile were more likely to develop spirometric COPD than those in lower RVCT/TLCCT tertile 849 Smokers without a clinical diagnosis of obstructive lung disease Air trapping, emphysema and airway disease
2018 Occhipinti et al Radiology Inspiratory and expiratory CT Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds was highly correlated with that obtained by coregistration analysis 224 COPD patients (GOLD 1–4) Emphysematous gas trapping
2018 Kirby et al Am J Respir Crit Care Med Inspiratory and expiratory CT CT total-airway-count was significantly reduced by 19% in both GOLD 1 and GOLD 2 compared with never smokers and by 17% in both GOLD 1 and GOLD 2 compared to at-risk participants after adjusting for LAA950 1184 Never-smokers, smokers with normal spirometry at risk for COPD and GOLD 1–2 Total number of airways quantified in vivo
2016 Bhatt et al Am J Respir Crit Care Med PRM Among GOLD 1–4 participants, for every additional 5% of lung affected by PRMfSAD or PRMemph, a significant decline in FEV1 was seen 1508 COPD subjects (GOLD 0–4) Emphysema and functional small airways disease
2018 Pompe et al Respir Med PRM and low dose CT PRMemph and PRMfSAD were both higher in COPD participants as compared to non-COPD participants and were higher with increasing GOLD stage 195 COPD patients (GOLD 0–4) Emphysema and functional small airways disease
2019 Vasilescu et al Am J Respir Crit Care Med PRM and micro-CT TB numbers were reduced; surviving TB had increased wall area, decreased circularity, reduced cross-sectional luminal area, and greater airway obstruction 55 End-stage COPD treated by lung transplantation Terminal bronchioles
2018 Tanabe et al Eur Respir J MDCT and micro-CT On micro-CT, there was a reduction in the number of terminal bronchioles as well as a decrease in the luminal areas, wall volumes and alveolar attachments to the walls of TB bronchioles 15 Patients with very severe COPD waiting for lung transplantation Small airway
2017 Tanabe et al Am J Respir Crit Care Med MDCT and micro-CT The preterminal bronchiolar length, wall volume, total volume (wall+lumen), lumen circularity and number of alveolar attachments were reduced in both centrilobular and panlobular emphysema compared to controls 20 Patients with COPD waiting for lung transplantation Preterminal bronchioles in emphysema
2018 Koo et al Lancet Respir Med Multiresolution CT and micro-CT The number of terminal bronchioles decreased by 40% in patients with GOLD 1 and 43% in patients with GOLD 2, the number of transitional bronchioles decreased by 56% in patients with GOLD 1 and 59% in patients with GOLD 2 34 COPD patients (GOLD 0–4) and patients who had undergone lung transplantation Terminal and transitional bronchioles
2016 Iyer et al Am J Respir Crit Care Med DECT After sildenafil administration, regional PBV-CV decreased in susceptible smokers, but did not decrease in nonsusceptible smokers, after adjusting for age and pack-years 17 PFT-normal current smokers Centriacinar emphysema
2016 Lee et al Eur Radiol DECT The xenon ventilation change correlates with the parenchymal attenuation change 52 COPD patients Parenchymal attenuation
2020 Humphries et al Radiology Thoracic CT Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-min walk distance, and St George's Respiratory Questionnaire score at univariate analysis 7143 COPDGene participants Emphysema pattern
COPD: Chronic obstructive pulmonary disease; COPDGene: Genetic Epidemiology of COPD; CT: Computed tomography; DECT: Dual-energy computed tomography; FEV1: Forced expiratory volume in 1 s; GOLD: Global Initiative for Chronic Obstructive Lung Disease; IgA: Immunoglobulin A; Pi10: Hypothetical airway with internal perimeter of 10 mm; FVC: Forced vital capacity; LAA950: Low-attenuation area below −950 Hounsfield units; MDCT: Multidetector row computed tomography; PBV-CV: Perfused blood volume-coefficients of variation; PRM: Parametric response mapping; PRMemph: Emphysema on parametric response mapping; PRMfSAD: Functional small airways disease on parametric response mapping; micro-CT: Micro-computed tomography; PFT: Pulmonary function test; QCT: Quantitative computed tomography; RVCT: CT-measured residual volume; TB: Terminal bronchioles; TLCCT: CT-measured total lung capacity.

Functional phenotypes of high-resolution computed tomography (HRCT)

HRCT is an ideal method for the detection and characterization of emphysema, as it offers a simple and easy way to quantify the degree of emphysema. Chest HRCT not only has a good correlation with lung function but also reflects the condition of small airway lesions.[37,38] A previous study compared 78 cases of patients with COPD at different stages of SAD. Evidently, both stenosis and occlusion in small airways appeared earlier than emphysema, indicating that CT examinations are superior in early diagnoses than detecting abnormalities of clinical symptoms and lung function index.[21]

Changes in small airways (both terminal and respiratory bronchi) are considered major early-stage COPD symptoms. However, for several reasons, CT is more difficult to use for small airway measurement than emphysema. CT imaging currently lacks the resolution to evaluate the thickness of small airway walls; thus, CT imaging studies mainly focus on larger airways and measure the thickness of the subsegmental bronchial wall in patients with COPD. It is difficult to apply CT in the routine evaluation of COPD in clinical practice due to the radiation exposure associated with CT.[39]

Studies have shown that patients with COPD who have mild emphysema and thickened bronchial walls, similar to asthmatic bronchitis and bronchial asthma, have more symptoms of wheezing.[40] Furthermore, the thickening of bronchial walls and airway stenosis incorporate more obvious airway inflammation, including more symptoms of cough and phlegm, which can be detected with CT. As for bronchial wall thickening, significant changes in emphysema can be used to estimate the mortality risk on a patient by patient basis.[41] Moreover, end-expiratory CT scans are a good way to assess gas retention in patients with COPD; most studies assess the presence of gas retention by low attenuation percentage at a threshold of either -856 or -850 Hounsfield units (HU) (low attenuation areas [LAA]-856 HU or LAA-850 HU).[42,43] Significantly, Murphy et al[44] found a correlation between LAA-850 HU and FEV1/forced vital capacity (FVC) and the ratio of FEV1 to predicted value (r = 0.85–0.90) in 216 smokers.

Overall, the above research demonstrated the use of HRCT as an important potential tool in the evaluation of COPD. In particular, HRCT may be a substitute for spirometry in certain situations.

Trapped gas CT phenotype

Accumulating evidence suggests that small airway disease is a key pathological feature of COPD. It is generally believed that air trapping is associated with small airway lesions, and that it is an important phenotype of COPD. Multiple studies have shown that lung function impairment is causatively related to trapped air and proximal airway stenosis in patients with asthma and COPD.[45]

Trapped air is an indirect HRCT measurement for small airway dysfunction. The lung lobules are overinflated due to airway obstruction.[46] Moreover, trapped air is linked to lung function decline in patients with COPD. However, this phenomenon was also observed in healthy non-smoking subjects without airflow blockages. In patients with COPD, the imaging findings are further complicated in the presence of emphysema. At least in clinical settings, air trapping remains a qualitative marker for SAD and may be beneficial for the diagnosis and monitoring of COPD. When all CT indices, including those of air trapping, emphysema, and airway disease, were included in the same model, only radiographically-measured residual volume (RV) to total lung capacity (TLC) ratio (RVCT/TLCCT) retained their clinical significance. Increased trapped air predicts the decline of lung function and the progression of COPD in smokers without airflow limitation.[47]

Although several studies have quantified gas retention in COPD, further investigations are required to determine the best approach. For example, the relative contribution of gas trapping, obtained by co-registration of inspiratory and expiratory CT scanning, can be determined by differences calculated using inspiratory and expiratory density thresholds. Intriguingly, CT scanning predicts the extent of emphysema in parallel with both the PFT and body mass index.[48]

Parametric response mapping phenotype

CT images have recently been used to obtain the inspiratory and expiratory phases to evaluate functional SAD (fSAD), which is caused by an emphysema-related low attenuation area and trapped air. The parametric response map (PRM) detection method was formed via processing of the inspiratory and expiratory phases of CT images.[49,50]

As a CT-based method to define phenotype of patients with COPD, PRM is capable of differentiating emphysema from non-emphysema-related trapped air, which aids in identifying the overall extent and localization of the disease. Given the relative contributions of fSAD and emphysema in COPD phenotypes, PRM serves as a complementary readout to PFTs and CT-based metrics, thereby aiding in the personalized diagnosis and management of COPD.[51]

PRMfSAD identifies areas of loss, narrowing, and obstruction of terminal bronchioles, thereby providing a non-invasive imaging methodology to identify small airway damage in COPD.[52] Recently, PRM has been applied to assess lung function decline after lung transplantation as well as diagnose complications related to hematopoietic stem cell transplantation.[53,54] Moreover, multiple genes were found to be associated with PRMEmph, indicating the essential role of PRM in recognizing the development of emphysema.[55]

Ventilatory function CT phenotype

Dual-energy CT lung ventilation imaging, based on xenon enhancement, can provide both anatomical information and ventilation function information for both the whole and local lung.[56] Xenon-enhanced dual-energy CT is now used extensively for clinical evaluations of lung function.[57] The exhaled xenon flow out (wash out) was negatively correlated with lung function, especially in the emphysema region, where there was a significant dynamic change in inhaled and exhaled xenon ventilation.

Regarding CT images, different ventilation modes are associated with different compositions of COPD on CT images, which is helpful in determining the etiology of COPD based on airflow limitation severity.[58] In fact, an alternative test has recently emerged, allowing multiple lung volumes to be scanned without contrast. For example, combined with advanced image processing techniques, the standardization of CT scans on total lung volume and residual air volume results in a good pulmonary ventilation function map and relevant assessment.

Micro-CT phenotype

The micro-CT allows extremely high resolution (1 μm/voxel), shows stunning images of the alveoli and terminal airways, and even realizes “virtual bronchoscopy” through alveolar ducts; it is an ideal method for dissecting the lung tissue, including small airways.[59] A combination of multidetector row CT, micro-CT, and histology provides more information on the pathological changes in small airway obstructions.[60,61]

In COPD, micro-CT can demonstrate loss of terminal bronchioles at early stages of the disease before microscopic emphysema. Moreover, it is possible for micro-CT to anatomically identify the terminal bronchioles, count the number of typical lung tissue samples, and subsequently calculate the total number of terminal bronchioles. Additionally, direct measurements, indicating the total number of bronchioles, as well as the average lumen cross-sectional area, provide a total cross-sectional area of terminal bronchioles in the lung.[62] Consistent with previous literature, time-dependent dynamic changes in micro-CT have been illustrated in a murine model of chronic cigarette smoke exposure.[63]

The combination of micro-CT and histology showed a decrease in the total number of terminal bronchioles, with thickening of bronchiolar walls in survivors. Some scholars have elegantly combined micro-CT with PRM in COPD research. Hoff et al[64] found that the surface area of fSAD (SfSAD) was the most robust and significant independent indicator of COPD in the clinic. Using micro-CT of human lung specimens, they also confirmed structural differences associated with unique SfSAD patterns; furthermore, they demonstrated that longitudinal feature alterations occurred with worsening pulmonary function, independently of an increase in disease extent.

Importantly, only small samples from lung tissue or experimental animals can be imaged by micro-CT. In addition, these samples are exposed to high doses of radiation, which are harmful to living tissues.[65] Although micro-CT is confined to research for the moment, this technology may play a role in the future.

Clinical Application of CT in Small Airway Detection

CT examinations often describe the anatomical structure in detail, with a minimum of 200 to 300 μm resolution, equivalent to the seventh to ninth grade bronchus of the lung.[66] Bronchioles can be automatically identified by HRCT through processing of the data. Furthermore, CT phenotypes have also been used to guide clinical treatment. For example, patients with COPD with the mixed phenotype are associated with more severe dyspnea and frequent hospitalizations than those with each of the remaining CT-based phenotypes. Thus, more attention and appropriate interventions should be paid to patients with mixed COPD phenotypes.[67] By using a novel multiresolution CT imaging protocol, a cross-sectional analysis showed that the development of airflow limitation in COPD involves progressive destruction and loss of the terminal and transitional bronchioles before a decline in lung function is observed.[68] This suggests that early intervention for disease modification in mild or moderate COPD cases is essential.

CT detection can also reflect the severity of COPD, as changes have a greater impact on airflow limitations in small- and medium-sized airways. The presence and severity of emphysema in patients with COPD can be estimated by the mathematical modeling of the airway function derived from standard spirometry and quantified by CT metrics and radiomics.[69] Quantitative CT (QCT) can detect short-time progression of emphysema in severe COPD.[70] The changes partly differed among lung lobes and airway generations, indicating QCT as a useful method to address the heterogeneity of COPD.

Additionally, CT scans can predict the progression of COPD and exacerbations. Among participants with airflow obstruction, every 5% increase in CT emphysema was independently associated with a 3.5 mL additional decline in FEV1 in mild-to-moderate stage COPD each year.[25] The rate of FEV1 decline is the greatest in functional SAD. Acute exacerbation of COPD is an acute event characterized by the worsening of respiratory symptoms. Patients with both emphysema and airway wall thickening are more symptomatic, have poorer pulmonary function, and have more frequent severe exacerbations.[71] These facts suggest that CT phenotypes may have important clinical applications.

CT examination of the small airway wall changes in COPD

Small airway walls in the lungs are usually less than 1 mm thick. In COPD patients, the infiltration of inflammatory cells results in increased airway wall thickness as well as changes in the density of small airways.[72] Upon CT scanning, the average attenuation value of the lung is lower than that of the small airways, and the peak attenuation value can be obtained via reconstruction algorithm involving airway wall thickness and density.

Washko et al[73] conducted a computer-based simulation study, showing that the attenuation value of airway walls can measure the changes in airway wall thickness and density. These findings are applicable to assess the thickness of airway walls in patients with COPD. The peak attenuation value has the highest correlation with the percentage of total airway area.[74]

Accurate assessment of small airways on thoracic quantitative CT scans in COPD

As the main site of airflow obstruction in COPD, the small airway cannot be directly displayed by CT. Actually, it can be obtained by quantitative analysis of gas density during exhalation. An increase in the total amount of residual gas reduces lung density at the end of the expiratory period, owing to the combined effect of decreased elastic pulmonary function caused by emphysema and increased airway resistance.[75] The variation amplitude of low attenuation value between inspiratory and expiratory scanning reflects the degree of trapped air and other pathological characteristics of small airways, as assessed by professional software.[76,77] Remarkably, emphysema occurs in patients with COPD and emphysema itself contains residual gas; thus, it is a confounding factor for the measurement of small airways.[78]

When the density threshold is fixed at −856 HU, the main limitation is the inability to distinguish low density areas that are associated with small airway destruction-related emphysema.[79] Although the air is retained in the lungs, the low-density area still looks normal on scanning, even if the small airway has been destroyed. Therefore, many researchers have focused on this method of small airway measurement. An attenuation value between −860 and −950 HU in inspiratory and expiratory CT scans suggests a relative volume change closely related to the prediction of FEV1/FVC and FEV1%. This indirect quantification of the airway has three advantages. Firstly, there is no need to consider the airway classification and the airflow limitation. Secondly, the CT attenuation value has greater correlation with pulmonary function than the bronchial size. Finally, it can provide regional assessment. However, there is still some overlap between the trapped air caused by SAD and trapped air caused by emphysema, and this interference cannot be eliminated.

The reliable and repeatable CT scans are difficult to obtain in patients with severe COPD. So, it is difficult to measure lung volume and the trapped air value. Undoubtedly, the correlation between continuous examinations may be limited. Multiple aspects are needed to calculate relative lung area or lung volume. Meanwhile, the results may be unreliable due to errors in post-processing.[80] Hersh et al[81] suggested that quantitative measurement based on paired inspiratory and expiratory chest CT scans can be used to assess small airway disease in smokers with and without COPD and to predict the prognosis of patients. For paired inspiratory and expiratory CT scans to quantify small airway obstruction, data collection should be pre-calibrated to help overcome possible errors.[82]

Factors affecting quantitative measurement

Although chest CT is beneficial in depicting regional lung function, air content in the lung, and emphysema (<−950 HU), imaging the small airways remains a challenge. Some of these limitations are as follows: static images cannot provide the required insight on lung function and dysfunction, limited resolution of clinical CT scans on lung architecture, and challenges that the biomedical community faces when trying to translate discovery into therapy. Such limitations may be addressed through novel image analysis techniques, upcoming CT-based and magnetic resonance imaging (MRI)-based technologies, closer ties between academia and industry, and an expanded endeavor to share data across the biomedical community.

The quantitative value of trapped air in expiratory scanning is notably affected by the expiratory level. However, the effect of expiration on trapped air has not been well elucidated. The scan should be followed by a strict breathing order, which can also be standardized by using a hilar lung scan that is technically cumbersome and not widely used in clinical practice. However, it is unknown whether this standardization will improve the repeatability of quantitative evaluation of air retention.[83]

Besides, the noise of low dose scans may affect quantitative measurements of trapped air, similar to that in the quantification of emphysema.[84] At present, there is no noise reduction filter to verify the effect of increasing image noise removal on the quantification of air capture; moreover, there is no research involving the quantitative measurement of sectional thickness of air retention using CT and other scanning parameters.[85] Furthermore, the presence of emphysema may affect the quantitative assessment of trapped air.

Artificial intelligence (AI) and deep learning

AI has become a hot topic in radiology. Thoracic imaging is an important domain for developing solutions based on AI.[86] Deep neural networks are one of the main techniques behind the explosion of AI as a powerful tool to improve the outcome prediction. Specifically, convolutional neural network analysis can identify smokers who have COPD and predict who are most likely to have acute respiratory disease events and those with the highest mortality risk.[87] Humphries et al[88] described that deep learning emphysema classifications were associated with impaired PFTs, 6-min walking distance, and St. George's Respiratory Questionnaire. Also, deep learning can help identify individuals at greater risk of mortality and may be a more sensitive indicator of the risk. Deep learning can be used to reliably automate quantification of air trapping and emphysema from inspiratory and expiratory CT. It facilitates staging of COPD severity, has a diagnostic performance comparable with that of spirometric GOLD staging, and provides further prognostic value when used in conjunction with GOLD staging.[89]

Conclusions and prospects

PFTs are regarded as the gold standard for the diagnosis of COPD. Although small airway changes occur at early stages of COPD, lung function often remains in the normal range. Small conducting airways were found to be a silent zone where disease could accumulate over time in combination with epidemiological evidence for a susceptible minority; this, alongside the understanding that cigarette smoking accelerates the age-related decline in FEV1, led to the search for specialized physiological tests to detect disease abnormalities earlier, in order to prevent disease progression.

Patients should be screened at early stages for the diagnosis, treatment, and prevention of COPD. The small airway CT phenotype, compared with the pulmonary function index, can accurately reflect the disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. However, the current available CT technology cannot directly display small airways. Therefore, air retention quantification is assessed by end-expiratory CT scans that can be used as an indirect measurement for SAD. Further investigations on functional CT phenotypes in small airway obstructions are warranted to shed new insights into the diagnosis and treatment of COPD.

Funding

This work was supported by the grants from the National Key Research and Development Program of China (No. 2018YFC1313602), Major International (Regional) Joint Research Project (No. 81820108001), National Natural Science Foundation of China (No. 81670029), Jiangsu Key Principal Investigator of Medicine (No. ZDRCA2016018), Project 333 for Cultivation of Young and Middle-aged Leading Talents (No. BRA2019078), Jiangsu Key Program of Social Development (No. BE2015651), and Nanjing Key Project of Science and Technology (No. 2019060002) (to L. Zhou), and Xuzhou Administration of Science and Technology (No. KC20082).

Conflicts of interest

None.

References

1. Global Initiative for Chronic Obstructive Lung Disease (GOLD). 2021 Global Strategy for the Diagnosis, Management and Prevention of COPD. Available from: URL:https://goldcopd.org/2021-gold-reports/. [Accessed November 25, 2020]
2. GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study. Lancet 2018; 392:1923–1994. doi: 10.1016/S0140-6736(18)32225-6.
3. Riley CM, Sciurba FC. Diagnosis and outpatient management of chronic obstructive pulmonary disease: a review. JAMA 2019; 321:786–797. doi: 10.1001/jama.2019.0131.
4. Wang C, Xu J, Yang L, Xu Y, Zhang X, Bai C, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet 2018; 391:1706–1717. doi: 10.1016/S0140-6736(18)30841-9.
5. Pompe E, Strand M, van Rikxoort EM, Hoffman EA, Barr RG, Charbonnier JP, et al. Five-year progression of emphysema and air trapping at CT in smokers with and those without chronic obstructive pulmonary disease: results from the COPDGene study. Radiology 2020; 295:218–226. doi: 10.1148/radiol.2020191429.
6. Liu H, Tang HY, Xu JY, Pang ZG. Small airway immunoglobulin A profile in emphysema-predominant chronic obstructive pulmonary disease. Chin Med J (Engl) 2020; 133:1915–1921. doi: 10.1097/CM9.0000000000000863.
7. Çolak Y, Afzal S, Nordestgaard BG, Vestbo J, Lange P. Prevalence, characteristics, and prognosis of early chronic obstructive pulmonary disease. The Copenhagen general population study. Am J Respir Crit Care Med 2020; 201:671–680. doi: 10.1164/rccm.201908-1644OC.
8. Polverino F, Sam A, Guerra S. COPD: to be or not to be, that is the question. Am J Med 2019; 132:1271–1278. doi: 10.1016/j.amjmed.2019.04.047.
9. Cruz T, López-Giraldo A, Noell G, Casas-Recasens S, Garcia T, Molins L, et al. Multi-level immune response network in mild-moderate chronic obstructive pulmonary disease (COPD). Respir Res 2019; 20:152doi: 10.1186/s12931-019-1105-z.
10. Higham A, Quinn AM, Cançado J, Singh D. The pathology of small airways disease in COPD: historical aspects and future directions. Respir Res 2019; 20:49doi: 10.1186/s12931-019-1017-y.
11. Gawlitza J, Haubenreisser H, Henzler T, Akin I, Schönberg S, Borggrefe M, et al. Finding the right spot: where to measure airway parameters in patients with COPD. Eur J Radiol 2018; 104:87–93. doi: 10.1016/j.ejrad.2018.05.003.
12. Brightling C, Greening N. Airway inflammation in COPD: progress to precision medicine. Eur Respir J 2019; 54:1900651doi: 10.1183/13993003.00651-2019.
13. Hoesterey D, Das N, Janssens W, Buhr RG, Martinez FJ, Cooper CB, et al. Spirometric indices of early airflow impairment in individuals at risk of developing COPD: spirometry beyond FEV1/FVC. Respir Med 2019; 156:58–68. doi: 10.1016/j.rmed.2019.08.004.
14. Kwon DS, Choi YJ, Kim TH, Byun MK, Cho JH, Kim HJ, et al. FEF25-75% values in patients with normal lung function can predict the development of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2020; 15:2913–2921. doi: 10.2147/COPD.S261732.
15. Stockley JA, Cooper BG, Stockley RA, Sapey E. Small airways disease: time for a revisit. Int J Chron Obstruct Pulmon Dis 2017; 12:2343–2353. doi: 10.2147/COPD.S138540.
16. Barnes PJ. Small airway fibrosis in COPD. Int J Biochem Cell Biol 2019; 116:105598doi: 10.1016/j.biocel.2019.105598.
17. Berger KI. Small airway disease syndromes. Piercing the quiet zone. Ann Am Thorac Soc 2018; 15:S26–S29. doi: 10.1513/AnnalsATS.201710-767KV.
18. Postma DS, Brightling C, Baldi S, Van den Berge M, Fabbri LM, Gagnatelli A, et al. Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study. Lancet Respir Med 2019; 7:402–416. doi: 10.1016/S2213-2600(19)30049-9.
19. Lavorini F, Pedersen S, Usmani OS. Aerosol drug management improvement team (ADMIT). Dilemmas, confusion, and misconceptions related to small airways directed therapy. Chest 2017; 151:1345–1355. doi: 10.1016/j.chest.2016.07.035.
20. Hogg JC, Paré PD, Hackett TL. The contribution of small airway obstruction to the pathogenesis of chronic obstructive pulmonary disease. Physiol Rev 2017; 97:529–552. doi: 10.1152/physrev.00025.2015.
21. McDonough JE, Yuan R, Suzuki M, Seyednejad N, Elliott WM, Sanchez PG, et al. Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med 2011; 365:1567–1575. doi: 10.1056/NEJMoa1106955.
22. Christenson SA, van den Berge M, Faiz A, Inkamp K, Bhakta N, Bonser LR, et al. An airway epithelial IL-17A response signature identifies a steroid-unresponsive COPD patient subgroup. J Clin Invest 2019; 129:169–181. doi: 10.1172/JCI121087.
23. Ostridge K, Williams N, Kim V, Bennett M, Harden S, Welch L, et al. Relationship between pulmonary matrix metalloproteinases and quantitative CT markers of small airways disease and emphysema in COPD. Thorax 2016; 71:126–132. doi: 10.1136/thoraxjnl-2015-207428.
24. Richmond BW, Du RH, Han W, Benjamin JT, van der Meer R, Gleaves L, et al. Bacterial-derived neutrophilic inflammation drives lung remodeling in a mouse model of chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol 2018; 58:736–744. doi: 10.1165/rcmb.2017-0329OC.
25. Bhatt SP, Soler X, Wang X, Murray S, Anzueto AR, Beaty TH, et al. Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2016; 194:178–184. doi: 10.1164/rccm.201511-2219OC.
26. Washko GR, Parraga G. COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging. Eur Respir J 2018; 52:1801570doi: 10.1183/13993003.01570-2018.
27. Mirza S, Benzo R. Chronic obstructive pulmonary disease phenotypes: implications for care. Mayo Clin Proc 2017; 92:1104–1112. doi: 10.1016/j.mayocp.2017.03.020.
28. Ju M, Short AD, Thompson P, Bakerly ND, Gkoutos GV, Tsaprouni L, et al. Annotating and detecting phenotypic information for chronic obstructive pulmonary disease. JAMA Open 2019; 2:261–271. doi: 10.1093/jamiaopen/ooz009.
29. Dirksen A, Wille MM. Computed tomography-based subclassification of chronic obstructive pulmonary disease. Ann Am Thorac Soc 2016; 13 Suppl 2:S114–S117. doi: 10.1513/AnnalsATS.201503-178KV.
30. Labaki WW, Martinez CH, Martinez FJ, Galbán CJ, Ross BD, Washko GR, et al. 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 2017; 196:1372–1379. doi: 10.1164/rccm.201703-0451PP.
31. Ostridge K, Wilkinson TM. Present and future utility of computed tomography scanning in the assessment and management of COPD. Eur Respir J 2016; 48:216–228. doi: 10.1183/13993003.00041-2016.
32. Subramanian DR, Gupta S, Burggraf D, Vom Silberberg SJ, Heimbeck I, Heiss-Neumann MS, et al. Emphysema- and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography. Eur Respir J 2016; 48:92–103. doi: 10.1183/13993003.01878-2015.
33. Maarsingh H, Bidan CM, Brook BS, Zuidhof AB, Elzinga C, Smit M, et al. Small airway hyperresponsiveness in COPD: relationship between structure and function in lung slices. Am J Physiol Lung Cell Mol Physiol 2019; 316:L537–546. doi: 10.1152/ajplung.00325.2018.
34. Charbonnier JP, Pompe E, Moore C, Humphries S, van Ginneken B, Make B, et al. Airway wall thickening on CT: relation to smoking status and severity of COPD. Respir Med 2019; 146:36–41. doi: 10.1016/j.rmed.2018.11.014.
35. Cheng T, Wan HY, Cheng QJ, Guo Y, Qian YR, Fan L, et al. Obvious emphysema on computed tomography during an acute exacerbation of chronic obstructive pulmonary disease predicts a poor prognosis. Intern Med J 2015; 45:517–526. doi: 10.1111/imj.12723.
36. Coste F, Benlala I, Dournes G, Girodet PO, Laurent F, Berger P. Assessing pulmonary hypertension in COPD. Is there a role for computed tomography. Int J Chron Obstruct Pulmon Dis 2019; 14:2065–2079. doi: 10.2147/COPD.S207363.
37. Hoffman EA, Lynch DA, Barr RG, van Beek EJ, Parraga G. IWPFI Investigators. Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes. J Magn Reson Imaging 2016; 43:544–557. doi: 10.1002/jmri.25010.
38. Lowe KE, Regan EA, Anzueto A, Austin E, Austin J, Beaty TH, et al. COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease. Chronic Obstr Pulm Dis 2019; 6:384–399. doi: 10.15326/jcopdf.6.5.2019.0149.
39. Han MK. Counterpoint: should chest CT be part of routine clinical care for COPD? No. Chest 2018; 154:1278–1281. doi: 10.1016/j.chest.2018.08.1057.
40. Choi S, Haghighi B, Choi J, Hoffman EA, Comellas AP, Newell JD, et al. Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respir Res 2017; 4:e000252doi: 10.1136/bmjresp-2017-000252.
41. Bodduluri S, Puliyakote A, Gerard SE, Reinhardt JM, Hoffman EA, Newell JD Jr, et al. Airway fractal dimension predicts respiratory morbidity and mortality in COPD. J Clin Invest 2018; 128:5374–5382. doi: 10.1172/JCI120693.
42. Zhang D, Guan Y, Fan L, Xia Y, Liu SY. Quantitative analysis of emphysema and air trapping at inspiratory and expiratory phase multi-slice spiral CT scan in smokers: correlation with pulmonary function test (in China). Natl Med J China 2018; 98:1467–1473. doi: 10.3760/cma.j.issn.0376-2491.2018.19.003.
43. Gawlitza J, Trinkmann F, Scheffel H, Fischer A, Nance JW, Henzler C, et al. Time to exhale: additional value of expiratory chest CT in chronic obstructive pulmonary disease. Can Respir J 2018; 2018:9493504doi: 10.1155/2018/9493504.
44. Murphy K, Pluim JP, van Rikxoort EM, de Jong PA, de Hoop B, Gietema HA, et al. Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT. Med Phys 2012; 39:1650–1662. doi: 10.1118/1.3687891.
45. Hartley RA, Barker BL, Newby C, Pakkal M, Baldi S, Kajekar R, et al. Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: a single-center study. J Allergy Clin Immunol 2016; 137:1413.e12–1422.e12. doi: 10.1016/j.jaci.2016.02.001.
46. Sorkness RL, Kienert C, O’Brien MJ, Fain SB, Jarjour NN. Compressive air trapping in asthma: effects of age, sex, and severity. J Appl Physiol (1985) 2019; 126:1265–1271. doi: 10.1152/japplphysiol.00924.2018.
47. Arjomandi M, Zeng S, Barjaktarevic I, Barr RG, Bleecker ER, Bowler RP, et al. Radiographic lung volumes predict progression to COPD in smokers with preserved spirometry in SPIROMICS. Eur Respir J 2019; 54:1802214doi: 10.1183/13993003.02214-2018.
48. Occhipinti M, Paoletti M, Bigazzi F, Camiciottoli G, Inchingolo R, Larici AR, et al. Emphysematous and nonemphysematous gas trapping in chronic obstructive pulmonary disease: quantitative CT findings and pulmonary function. Radiology 2018; 287:683–692. doi: 10.1148/radiol.2017171519.
49. Galbán CJ, Han MK, Boes JL, Chughtai KA, Meyer CR, Johnson TD, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 2012; 18:1711–1715. doi: 10.1038/nm.2971.
50. Pompe E, Galbán CJ, Ross BD, Koenderman L, Ten Hacken NH, Postma DS, et al. Parametric response mapping on chest computed tomography associates with clinical and functional parameters in chronic obstructive pulmonary disease. Respir Med 2017; 123:48–55. doi: 10.1016/j.rmed.2016.11.021.
51. MacNeil JL, Capaldi D, Westcott AR, Eddy RL, Barker AL, McCormack DG, et al. Pulmonary imaging phenotypes of chronic obstructive pulmonary disease using multiparametric response maps. Radiology 2020; 295:227–236. doi: 10.1148/radiol.2020191735.
52. Vasilescu DM, Martinez FJ, Marchetti N, Galbán CJ, Hatt C, Meldrum CA, et al. Noninvasive imaging biomarker identifies small airway damage in severe chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2019; 200:575–581. doi: 10.1164/rccm.201811-2083OC.
53. Cheng GS, Selwa KE, Hatt C, Ram S, Fortuna AB, Guerriero M, et al. Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation. Am J Transplant 2020; 20:2198–2205. doi: 10.1111/ajt.15814.
54. Belloli EA, Degtiar I, Wang X, Yanik GA, Stuckey LJ, Verleden SE, et al. Parametric response mapping as an imaging biomarker in lung transplant recipients. Am J Respir Crit Care Med 2017; 195:942–952. doi: 10.1164/rccm.201604-0732OC.
55. Rathnayake S, Hoesein F, Galban CJ, Ten Hacken N, Oliver B, van den Berge M, et al. Gene expression profiling of bronchial brushes is associated with the level of emphysema measured by computed tomography-based parametric response mapping. Am J Physiol Lung Cell Mol Physiol 2020; 318:L1222–L1228. doi: 10.1152/ajplung.00051.2020.
56. Machida H, Tanaka I, Fukui R, Shen Y, Ishikawa T, Tate E, et al. Dual-energy spectral CT: various clinical vascular applications. Radiographics 2016; 36:1215–1232. doi: 10.1148/rg.2016150185.
57. Hwang HJ, Hoffman EA, Lee CH, Goo JM, Levin DL, Kauczor HU, et al. The role of dual-energy computed tomography in the assessment of pulmonary function. Eur J Radiol 2017; 86:320–334. doi: 10.1016/j.ejrad.2016.11.010.
58. Iyer KS, Newell JD Jr, Jin D, Fuld MK, Saha PK, Hansdottir S, et al. Quantitative dual-energy computed tomography supports a vascular etiology of smoking-induced inflammatory lung disease. Am J Respir Crit Care Med 2016; 193:652–661. doi: 10.1164/rccm.201506-1196OC.
59. Young HM, Eddy RL, Parraga G. MRI and CT lung biomarkers: towards an in vivo understanding of lung biomechanics. Clin Biomech (Bristol, Avon) 2019; 66:107–122. doi: 10.1016/j.clinbiomech.2017.09.016.
60. Tanabe N, Vasilescu DM, Kirby M, Coxson HO, Verleden SE, Vanaudenaerde BM, et al. Analysis of airway pathology in COPD using a combination of computed tomography, micro-computed tomography and histology. Eur Respir J 2018; 51:1701245doi: 10.1183/13993003.01245-2017.
61. Tanabe N, Vasilescu DM, McDonough JE, Kinose D, Suzuki M, Cooper JD, et al. Micro-computed tomography comparison of preterminal bronchioles in centrilobular and panlobular emphysema. Am J Respir Crit Care Med 2017; 195:630–638. doi: 10.1164/rccm.201602-0278OC.
62. Kobayashi S, Fujinawa R, Ota F, Kobayashi S, Angata T, Ueno M, et al. A single dose of lipopolysaccharide into mice with emphysema mimics human chronic obstructive pulmonary disease exacerbation as assessed by micro-computed tomography. Am J Respir Cell Mol Biol 2013; 49:971–977. doi: 10.1165/rcmb.2013-0074OC.
63. Sasaki M, Chubachi S, Kameyama N, Sato M, Haraguchi M, Miyazaki M, et al. Evaluation of cigarette smoke-induced emphysema in mice using quantitative micro-computed tomography. Am J Physiol Lung Cell Mol Physiol 2015; 308:L1039–1045. doi: 10.1152/ajplung.00366.2014.
64. Hoff BA, Pompe E, Galbán S, Postma DS, Lammers JJ, Ten Hacken N, et al. CT-based local distribution metric improves characterization of COPD. Sci Rep 2017; 7:2999doi: 10.1038/s41598-017-02871-1.
65. Berghen N, Dekoster K, Marien E, Dabin J, Hillen A, Wouters J, et al. Radiosafe micro-computed tomography for longitudinal evaluation of murine disease models. Sci Rep 2019; 9:17598doi: 10.1038/s41598-019-53876-x.
66. Lindenmaier TJ, Kirby M, Paulin G, Mielniczuk L, Cunningham IA, Mura M, et al. Pulmonary artery abnormalities in ex-smokers with and without airflow obstruction. COPD 2016; 13:224–234. doi: 10.3109/15412555.2015.1074666.
67. Van Tho N, Ogawa E, Trang le TH, Ryujin Y, Kanda R, Nakagawa H, et al. A mixed phenotype of airway wall thickening and emphysema is associated with dyspnea and hospitalization for chronic obstructive pulmonary disease. Ann Am Thorac Soc 2015; 12:988–996. doi: 10.1513/AnnalsATS.201411-501OC.
68. Koo HK, Vasilescu DM, Booth S, Hsieh A, Katsamenis OL, Fishbane N, et al. Small airways disease in mild and moderate chronic obstructive pulmonary disease: a cross-sectional study. Lancet Respir Med 2018; 6:591–602. doi: 10.1016/S2213-2600(18)30196-6.
69. Occhipinti M, Paoletti M, Bartholmai BJ, Rajagopalan S, Karwoski RA, Nardi C, et al. Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD. Respir Res 2019; 20:101doi: 10.1186/s12931-019-1049-3.
70. Konietzke P, Wielpütz MO, Wagner WL, Wuennemann F, Kauczor HU, Heussel CP, et al. Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval. Eur Radiol 2020; 30:2502–2512. doi: 10.1007/s00330-019-06577-y.
71. Lim JU, Kim EK, Lim SY, Lee JH, Lee JS, Lee SD, et al. Mixed phenotype of emphysema and airway wall thickening is associated with frequent exacerbation in chronic obstructive pulmonary disease patients. Int J Chron Obstruct Pulmon Dis 2019; 14:3035–3042. doi: 10.2147/COPD.S227377.
72. Verleden SE, Kirby M, Everaerts S, Vanstapel A, McDonough JE, Verbeken EK, et al. Small airway loss in the physiologically ageing lung: a cross-sectional study in unused donor lungs. Lancet Respir Med 2021; 9:167–174. doi: 10.1016/S2213-2600(20)30324-6.
73. Washko GR, Coxson HO, O’Donnell DE, Aaron SD. CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise. Lancet Respir Med 2017; 5:903–908. doi: 10.1016/S2213-2600(17)30345-4.
74. Gelb AF, Yamamoto A, Verbeken EK, Nadel JA. Unraveling the pathophysiology of the asthma-COPD overlap syndrome: unsuspected mild centrilobular emphysema is responsible for loss of lung elastic recoil in never smokers with asthma with persistent expiratory airflow limitation. Chest 2015; 148:313–320. doi: 10.1378/chest.14-2483.
75. Dransfield MT, Kunisaki KM, Strand MJ, Anzueto A, Bhatt SP, Bowler RP, et al. Acute exacerbations and lung function loss in smokers with and without chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2017; 195:324–330. doi: 10.1164/rccm.201605-1014OC.
76. Tomita H, Yamashiro T, Matsuoka S, Matsushita S, Nakajima Y. Correlation between heart size and emphysema in patients with chronic obstructive pulmonary disease: CT-based analysis using inspiratory and expiratory scans. Chron Respir Dis 2018; 15:272–278. doi: 10.1177/1479972317741896.
77. Onoe R, Yamashiro T, Handa H, Azagami S, Matsuoka S, Inoue T, et al. 3D-measurement of tracheobronchial angles on inspiratory and expiratory chest CT in COPD: respiratory changes and correlation with airflow limitation. Int J Chron Obstruct Pulmon Dis 2018; 13:2399–2407. doi: 10.2147/COPD.S165824.
78. Yoon HY, Kim TH, Seo JB, Lee SM, Lim S, Lee HN, et al. Effects of emphysema on physiological and prognostic characteristics of lung function in idiopathic pulmonary fibrosis. Respirology 2019; 24:55–62. doi: 10.1111/resp.13387.
79. Nambu A, Zach J, Schroeder J, Jin G, Kim SS, Kim YI, et al. Quantitative computed tomography measurements to evaluate airway disease in chronic obstructive pulmonary disease: relationship to physiological measurements, clinical index and visual assessment of airway disease. Eur J Radiol 2016; 85:2144–2151. doi: 10.1016/j.ejrad.2016.09.010.
80. de Boer E, Nijholt IM, Jansen S, Edens MA, Walen S, van den Berg J, et al. Optimization of pulmonary emphysema quantification on CT scans of COPD patients using hybrid iterative and post processing techniques: correlation with pulmonary function tests. Insights Imaging 2019; 10:102doi: 10.1186/s13244-019-0776-9.
81. Hersh CP, Washko GR, Estépar RS, Lutz S, Friedman PJ, Han MK, et al. Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD. Respir Res 2013; 14:42doi: 10.1186/1465-9921-14-42.
82. Weinheimer O, Hoff BA, Fortuna AB, Fernández-Baldera A, Konietzke P, Wielpütz MO, et al. Influence of inspiratory/expiratory CT registration on quantitative air trapping. Acad Radiol 2019; 26:1202–1214. doi: 10.1016/j.acra.2018.11.001.
83. Sanders K, Ash SY, Washko GR, Mottaghy FM, Schols A. Imaging approaches to understand disease complexity: chronic obstructive pulmonary disease as a clinical model. J Appl Physiol (1985) 2018; 124:512–520. doi: 10.1152/japplphysiol.00143.2017.
84. Messerli M, Ottilinger T, Warschkow R, Leschka S, Alkadhi H, Wildermuth S, et al. Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT – intra-individual comparison with standard dose CT. Eur J Radiol 2017; 91:1–9. doi: 10.1016/j.ejrad.2017.03.003.
85. Ohno Y, Koyama H, Seki S, Kishida Y, Yoshikawa T. Radiation dose reduction techniques for chest CT: principles and clinical results. Eur J Radiol 2019; 111:93–103. doi: 10.1016/j.ejrad.2018.12.017.
86. Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol 2020; 123:108774doi: 10.1016/j.ejrad.2019.108774.
87. González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med 2018; 197:193–203. doi: 10.1164/rccm.201705-0860OC.
88. Humphries SM, Notary AM, Centeno JP, Strand MJ, Crapo JD, Silverman EK, et al. Deep learning enables automatic classification of emphysema pattern at CT. Radiology 2020; 294:434–444. doi: 10.1148/radiol.2019191022.
89. Hasenstab KA, Yuan N, Retson T, Conrad DJ, Kligerman S, Lynch DA, et al. Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network. Radiol Cardiothorac Imaging 2021; 3:e200477doi: 10.1148/ryct.2021200477.
Keywords:

Chronic obstructive pulmonary disease; Small airway obstruction; Computed tomography; Phenotype; Pulmonary function test

Copyright © 2021 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license.