Air trapping on computed tomography: regional versus diffuse

Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods

OBJECTIVE

Diagnosis of small airway disease on computed tomography (CT) scans is challenging in patients with a history of chemical warfare exposure. We developed a software package based on different methodologies to identify and quantify small airway disease in CT images. The primary aim was to identify the best automatic methodology for detecting small airway disease in CT scans of Iran-Iraq War victims of chemical warfare.

METHODS

This retrospective case-control study enrolled 46 patients with a history of chemical warfare exposure and 27 controls with inspiratory/expiratory (I/E) CT scans and spirometry tests. Image data were automatically segmented, and inspiratory images were registered into the expiratory images' frame using the locally developed software. Parametric response mapping (PRM) and air trapping index (ATI) mapping were performed on the CT images. Conventional QCT methods, including expiratory/inspiratory mean lung attenuation (E/I MLA) ratio, normal density E/I (ND E/I) MLA ratio, attenuation volume Index (AVI), %low attenuation areas (LAA) < -856 in exhale scans, and %LAA < -950 in inhale scans were also computed. QCT measurements were correlated with spirometry results and compared across the two study groups.

RESULTS

The correlation analysis showed a significant negative relationship between three air trapping (AT) measurements (PRM, ATI, and %LAAExp < -856) and spirometry parameters (Fev1, Fvc, Fev1/Fvc, and MMEF). Moreover, %LAAExp < -856 had the highest significant negative correlation with Fev1/Fvc (r = -0.643, P-value < 0.001). Three AT measurements demonstrated a significant difference between the study groups. The E/I ratio was also significantly different between the two groups (P-value < 0.001). Binary logistic regression models showed PRMFsad, %LAAExp < -856, and ATI as significant and strong predictors of the study outcome. Optimal cut-points for PRMFsad = 19%, %LAAExp < -856 = 23%, and ATI = 27% were identified to classify the participants into two groups with high accuracy.

CONCLUSION

QCT methods, including PRM, ATI, and %LAAExp < -856 can greatly advance the identification and quantification of SAD in chemical warfare victims. The results should be verified in well-designed prospective studies involving a large population.

Air trapping in small airway diseases: A review of imaging technique and findings with an overview of small airway diseases

Air trapping is a common finding radiologists encounter on CT imaging of the thorax. This term is used when there are geographic areas of differing attenuation within the lung parenchyma. Most commonly, this is the result of abnormal retention of air due to complete or partial airway obstruction from small airway pathologies. Perfusional differences due to underlying vascular diseases could also result in these appearances, and hence, inspiratory and full expiratory phase CT studies are required to accurately diagnose air trapping. It is important to note that this can occasionally be present in healthy patients. Multiple diseases are associated with air trapping. Determining the aetiology relies on accurate patient history and concomitant findings on CT. There is currently no consensus on accurate assessment of the severity of air trapping. The ratio of mean lung density between expiration and inspiration on CT and the change in lung volume have demonstrated a positive correlation with the presence of small airway disease. Treatment and resultant patient outcome depend on the underlying aetiology, and hence, radiologists need to be familiar with the common causes of air trapping. This paper outlines the most common disease processes leading to air trapping, including Constrictive bronchiolitis, Hypersensitivity pneumonitis, DIPNECH, and Post-infectious (Swyer-James/Macleod). Various diseases result in the air trapping pattern seen on the expiratory phase CT scan of the thorax. Combining patient history with other concomitant imaging findings is essential for accurate diagnosis and to further guide management.

HRCT findings in secondhand smokers with respiratory symptoms

PURPOSE

The present study aims to investigate the occurrence and severity of HRCT abnormalities in symptomatic never-smokers, passively exposed to cigarette smoke.

MATERIALS AND METHODS

A total of 135 never-smokers with respiratory symptoms, without underlying lung disease, underwent paired inspiratory-expiratory HRCT and completed the secondhand smoking (SHS) exposure scale questionnaire. Individuals passively exposed to tobacco consisted the secondhand smoking group (SHS group) (n = 68); the remaining 67 controls were never exposed to SHS. Statistical analysis was performed using the Kolmogorov-Smirnov, x² and Pearson Point-Biserial correlation tests. P < 0.05 was statistically significant.

RESULTS

HRCT findings in SHS group included bronchial wall thickening (98.5%), mild cylindrical bronchiectasis (44%), ground-glass opacities (17.6%), and mosaic attenuation pattern (53%). Air trapping occurred in 65% of expiratory scans. Differences in occurrence of findings between SHS group participants and controls were statistically significant (p < 0.0001). There was significant correlation between exposure to tobacco smoke in hours/day and in number of cigarettes/day and the presence of mosaic attenuation pattern, ground-glass opacities, cylindrical bronchiectasis and air trapping, respectively (p < 0.05). Cumulative SHS exposure in years was also strongly correlated with the presence of ground-glass opacities, mosaic attenuation pattern, cylindrical bronchiectasis, and air trapping, as well as with the frequency of respiratory symptoms (p < 0.05).

CONCLUSION

HRCT findings attributed to SHS are mostly related to airway involvement and are correlated with the duration and grade of exposure to environmental tobacco smoke. SHS followed by lifestyle adjustments should be considered in symptomatic non-smokers with such HRCT findings.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

Quantitative inspiratory-expiratory chest CT findings in COVID-19 survivors at the 6-month follow-up

We evaluated pulmonary sequelae in COVID-19 survivors by quantitative inspiratory-expiratory chest CT (QCT) and explored abnormal pulmonary diffusion risk factors at the 6-month follow-up. This retrospective study enrolled 205 COVID-19 survivors with baseline CT data and QCT scans at 6-month follow-up. Patients without follow-up pulmonary function tests were excluded. All subjects were divided into group 1 (carbon monoxide diffusion capacity [DL_CO] < 80% predicted, n = 88) and group 2 (DL_CO ≥ 80% predicted, n = 117). Clinical characteristics and lung radiological changes were recorded. Semiquantitative total CT score (0-25) was calculated by adding five lobes scores (0-5) according to the range of lesion involvement (0: no involvement; 1: < 5%; 2: 5-25%; 3: 26-50%; 4: 51-75%; 5: > 75%). Data was analyzed by two-sample t-test, Spearman test, etc. 29% survivors showed air trapping by follow-up QCT. Semiquantitative CT score and QCT parameter of air trapping in group 1 were significantly greater than group 2 (p < 0.001). Decreased DL_CO was negatively correlated with the follow-up CT score for ground-glass opacity (r = - 0.246, p = 0.003), reticulation (r = - 0.206, p = 0.002), air trapping (r = - 0.220, p = 0.002) and relative lung volume changes (r = - 0.265, p = 0.001). COVID-19 survivors with lung diffusion deficits at 6-month follow-up tended to develop air trapping, possibly due to small-airway impairment.

Reader Perceptions and Impact of AI on CT Assessment of Air Trapping

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021.

Unilateral Chronic Lung Allograft Dysfunction Assessed by Biphasic Computed Tomographic Volumetry in Bilateral Living-donor Lobar Lung Transplantation

Background

Early diagnosis of unilateral chronic lung allograft dysfunction (CLAD) is difficult because the unaffected contralateral lung functions as a reservoir in bilateral living-donor lobar lung transplantation (LDLLT). We previously reported the usefulness of ¹³³Xe ventilation scintigraphy for detection of unilateral change, but the supply of ¹³³Xe has been stopped globally. The present study aimed to examine the usefulness of inspiratory and expiratory computed tomography (I/E CT) volumetry for detection of unilateral change in CLAD patients.

Methods

This was a retrospective single-center, observational study using prospectively collected data. A total of 58 patients who underwent bilateral LDLLT from August 2008 to February 2017 were analyzed. Respiratory function tests, I/E CT were prospectively conducted. ΔLung volume was defined as the value obtained by subtracting expiratory lung volume from inspiratory lung volume.

Results

Fourteen (24%) cases were clinically diagnosed with CLAD, of which 10 (71%) were diagnosed as unilateral CLAD. ΔLung volume of bilateral lungs strongly correlated with forced vital capacity (r = 0.92, P < 0.01) and forced expiratory volume in 1 second (r = 0.80, P < 0.01). Regardless the phenotypes (bronchiolitis obliterans syndrome or restrictive allograft syndrome) of CLAD, Δlung volume onset/baseline significantly decreased compared with that in the non-CLAD group. Among the 10 unilateral CLAD patients, 3 with clinically suspected unilateral rejection yet did not show a 20% decline in forced expiratory volume in 1 second. In 2 of these, Δlung volume of unilateral lungs on the rejection side decreased by 20% or more.

Conclusions

Our findings suggest that I/E CT volumetry may be useful for assessment and early diagnosis of unilateral CLAD after bilateral LDLLT.

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.