Emphysema Subtypes Research Articles

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.

Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function. We employed graph modeling on 2,643 COPDGene participants (aged 45 to 80 years, 51.25% female, 35.1% African Americans; enrollment 11/2007-4/2011) with smoking history but normal spirometry at study enrollment to identify variables that are directly linked to future lung function abnormalities. We developed logistic regression and random forest predictive models for distinguishing individuals who maintain lung function from those who decline. Of the 131 variables analyzed, 6 were identified as informative to future lung function abnormalities, namely forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness in a 10 mm radius (Pi10), severe emphysema, age, sex, and height. We investigated whether these features predict individuals leaving GOLD 0 status (normal spirometry according to Global Initiative for Obstructive Lung Disease (GOLD) criteria). Linear models, trained with these features, were quite predictive (area under receiver operator characteristic curve or AUROC = 0.75). Random forest predictors performed similarly to logistic regression (AUROC = 0.7), indicating that no significant nonlinear effects were present. The results were externally validated on 150 participants from Specialized Center for Clinically Oriented Research (SCCOR) cohort (aged 45 to 80 years, 52.7% female, 4.7% African Americans; enrollment: 7/2007-12/2012) (AUROC = 0.89). The main limitation of longitudinal studies with 5- and 10-year follow-up is the introduction of mortality bias that disproportionately affects the more severe cases. However, our study focused on spirometrically normal individuals, who have a lower mortality rate. Another limitation is the use of strict criteria to define spirometrically normal individuals, which was unavoidable when studying factors associated with changes in normalized forced expiratory volume in 1 s (FEV1%predicted) or the ratio of FEV1/FVC (forced vital capacity). This study took an agnostic approach to identify which baseline measurements differentiate and predict the early stages of lung function decline in individuals with previous smoking history. Our analysis suggests that emphysema affects obstruction onset, while airway predominant pathology may play a more important role in future FEV1 (%predicted) decline without obstruction, and FEF25-75% may affect both.

AUTOMATED CLASSIFICATION OF EMPHYSEMA USING CONVOLUTIONAL NEURAL NETWORKS

The analysis of CT scans for emphysema diagnosis can be formulated as a computer vision problem, where predefined patterns are used to automatically classify the images. This paper investigates a method for classifying emphysema subtypes in High-Resolution Computed Tomography (HRCT) scans using convolutional neural networks (CNNs). This classification is crucial for accurate diagnosis and management of emphysema, a chronic lung disease causing shortness of breath. Our approach leverages transfer learning to extract informative features from HRCT images labeled as centrilobular (CLE), paraseptal (PLE), panlobular (PSE), and non-emphysematous (NT). We evaluate various CNN architectures, with InceptionV3 demonstrating exceptional performance, achieving an accuracy of 98% while effectively distinguishing between all subtypes. The trained InceptionV3 model is then integrated into a user-friendly front-end application developed using Streamlit and ngrok. This application allows real-time emphysema classification from HRCT scans, potentially aiding clinicians in diagnosis and treatment decisions. Keywords:- Emphysema classification ,Transfer Learning,CNN,InceptionV3,Streamlit , Ngrok

Comparative study of bone mineral density, phenotypes of emphysema in patients with or without established diagnosis of chronic airway disease

Background: Bone thinning and emphysema are established diseases caused by tobacco-related products proven beyond doubt. A paucity of literature available to establish the association between them. Objectives: To explore the association between the different emphysema subtypes and the presence of low bone mineral density among a population of active or former smokers with and without an established diagnosis of chronic obstructive pulmonary disease (COPD). Methods: 100 active and former smokers with chronic airway disease attending the Pulmonology Outpatient Department were identified, and they were administered clinical questionnaires, pulmonary function tests, chest computed tomography, and DEXA scans. Appropriate statistical tests were performed to analyze the association between the different emphysema subtypes, low bone mineral density, and smoking in the study participants. Results: Out of 100 samples, 80 % of the patients had low BMD (25 had osteoporosis, and 55 had osteopenia). Emphysema was more frequent and severe in patients with osteoporosis (63.2 vs. 36.8%, p=<0.01). For analyzing significant co-factors, a multivariable analysis was done, which yielded Body Mass Index (Odds ratio of 0.89 with a 95% Confidence Interval of 0.67–1.23) and the presence of centrilobular emphysema as most commonly associated with osteoporosis. (Odds ratio of 35.25, with a 95% confidence interval of 3.23 to 401.63). Conclusion: Patients having increased Body Mass Index (BMI) and the presence of centrilobular emphysema are having osteoporosis irrespective of their smoking status, which implicates BMI, and centrilobular emphysema has to be considered while evaluating COPD patients.

DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images

Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D lung CT images to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale datasets of lung CT scans show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework. Our codes are available at https://github.com/batmanlab/DrasCLR.

Emphysema subtyping on thoracic computed tomography scans using deep neural networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.

The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network.

This study investigated the influence of iterative reconstruction (IR) methods on computed tomography (CT) images when training convolutional neural network (CNN) models to diagnose pulmonary emphysema. To evaluate the influence of the IR algorithm on CNN, the present study comprised two steps: the comparison of noise reduction by IR algorithms using phantom examinations and the change in performance of CNN with IR algorithms using patient data. We retrospectively analyzed 97 patients. Raw CT data were reconstructed using the filtered back-projection (FBP) and adaptive statistical iterative reconstruction V (ASIR-V) algorithms with blending levels of 30%, 50%, and 70%. The models were trained using reconstructed CT images and were named the FBP, ASIR-V30, ASIR-V50, and ASIR-V70 models. The mean and the standard deviation of the CT values were 11.3 ± 21.2 at FBP, 11.0 ± 17.3 at ASIR-V30, 11.0 ± 14.4 at ASIR-V50, and 11.0 ± 11.8 at ASIR-V70. For all the evaluation metrics, the best values were obtained with the FBP model applied to the ASIR-V70 test images. The worst values were obtained with the ASIR-V70 model applied to the FBP test images. The model trained with FBP images exhibited significantly better performance than the models trained using IR images. The reduction in image noise with the IR algorithm on the test images contributed to improving the accuracy of the classification of emphysema subtypes using CNN.

Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans

BackgroundTreatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover...

Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

AbstractA computer‐assisted diagnosis framework to examine computed tomography (CT) slices for diagnosing pulmonary emphysema is designed. Partitioning of lung tissues and regions of Interest (ROIs) from the CT slices is achieved using Spatial Intuitionistic Fuzzy C‐Means (SIFCM) clustering algorithm. Shape features, texture features, and run‐length features are extracted from each ROI. Feature selection is performed as a wrapper technique by employing manta ray foraging optimization (MRFO) algorithm and random forest (RF) classifier. A backpropagation neural network (BPNN) using gradient descent is used to train the selected features. Adaptive butterfly optimization algorithm (ABOA) is used to fix the optimal topology and parameters, namely weights and biases of the neural network. The BPNN classifier is initialized with the optimized topology and parameters obtained by the ABOA. The developed framework attained an accuracy of 87.52% when tested on an emphysema dataset, which outperforms the BPNN classifier in terms of accuracy, specificity, precision, and recall.

Stronger Associations of Centrilobular Than Paraseptal Emphysema With Longitudinal Changes in Diffusing Capacity and Mortality in COPD

The factors associated with longitudinal changes in diffusing capacity remain unclear among patients with COPD. Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are major emphysema subtypes that may have distinct clinical-physiological impacts in these patients. Are CLE and PSE differently associated with longitudinal changes in diffusing capacity and mortality in patients with COPD? This pooled analysis included 399 patients with COPD from two prospective observational COPD cohorts. CLE and PSE were visually assessed on CT scan according to the Fleischner Society statement. The diffusing capacity and transfer coefficient of the lung for carbon monoxide (Dlco and KCO) and FEV1 were evaluated at least annually over a 5-year period. Mortality was recorded over 10 years. Longitudinal changes in FEV1, Dlco, and KCO and mortality were compared between mild or less severe and moderate or more severe CLE and between present and absent PSE in each Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. The Dlco and KCO decline was weakly associated with FEV1 and greater in GOLD stage 3 or higher than in GOLD stages 1 and 2. Furthermore, moderate or more severe CLE, but not present PSE, was associated with steeper declines in Dlco for GOLD stages 1 and 3 or higher and KCO for all GOLD stages independent of age, sex, height, and smoking history. The moderate or more severe CLE, but not present PSE, was associated with additional FEV1 decline and higher 10-year mortality among patients with GOLD stage 3 or higher. A CT scan finding of moderate or more severe CLE, but not PSE, was associated with a subsequent accelerated impairment in diffusing capacity and higher long-term mortality in severe GOLD stage among patients with COPD.

Radiologic Subtypes and Treatment Outcome of Unclassifiable Type Mycobacterium avium Complex Pulmonary Disease.

Although the "unclassifiable type" is categorized as one of the radiologic classifications in Mycobacterium avium complex (MAC) pulmonary disease (PD), there have been few studies of this type thus far. We aimed to investigate the radiologic subtypes and treatment outcome of unclassifiable type MAC-PD. Ninety-six patients with unclassifiable type MAC-PD who initiated a macrolide-containing regimen from 2001 to 2020 were identified at a tertiary referral center in South Korea. Among these 96 patients, 1-year culture conversion rate was analyzed for 48 patients who received standard treatment (three-drug oral-antibiotic combination with or without an injectable agent) for ≥ 1 year. The mean age of the 96 patients was 65.4 ± 10.8 years, and 72.9% of them were male. These patients were classified into four major radiologic subtypes; the most common subtype was the focal cavity subtype (n = 31, 32.3%), followed by the focal mass or nodule (n = 23, 24.0%), consolidation upon emphysema (n = 21, 21.9%), and bronchiolitis (n = 21, 21.9%) subtypes. For the 48 patients who received standard treatment for ≥ 1 year, the overall rate of culture conversion at 1-year was 93.8%. All patients in the focal cavity subtype and focal mass or nodule subtype categories achieved 1-year culture conversion. Additionally, 1-year culture conversion rate was 92.9% in consolidation upon emphysema subtype and 75.0% in bronchiolitis subtype. Unclassifiable type MAC-PD can be radiologically further categorized into four major radiologic subtypes. The treatment outcome of all of these subtypes seems to be favorable.

Mapping Alveolar Oxygen Partial Pressure in COPD Using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.

Chronic obstructive pulmonary disease (COPD) and emphysema are characterized by functional and structural damage which increases the spaces for gaseous diffusion and impairs oxygen exchange. Here we explore the potential for hyperpolarized (HP) 3He MRI to characterize lung structure and function in a large-scale population-based study. Participants (n = 54) from the Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study, a nested case-control study of COPD among participants with 10+ packyears underwent HP 3He MRI measuring pAO2, apparent diffusion coefficient (ADC), and ventilation. HP MRI measures were compared to full-lung CT and pulmonary function testing. High ADC values (>0.4 cm2/s) correlated with emphysema and heterogeneity in pAO2 measurements. Strong correlations were found between the heterogeneity of global pAO2 as summarized by its standard deviation (SD) (p < 0.0002) and non-physiologic pAO2 values (p < 0.0001) with percent emphysema on CT. A regional study revealed a strong association between pAO2 SD and visual emphysema severity (p < 0.003) and an association with the paraseptal emphysema subtype (p < 0.04) after adjustment for demographics and smoking status. HP noble gas pAO2 heterogeneity and the fraction of non-physiological pAO2 results increase in mild to moderate COPD. Measurements of pAO2 are sensitive to regional emphysematous damage detected by CT and may be used to probe pulmonary emphysema subtypes. HP noble gas lung MRI provides non-invasive information about COPD severity and lung function without ionizing radiation.

Long-term mortality in ischemic stroke patients with concomitant chronic obstructive pulmonary disease

BackgroundLong-term mortality in ischemic stroke patients with concomitant COPD has been largely unexplored. This study aimed to compare long-term all-cause mortality in ischemic stroke patients with and without COPD. MethodsThis was a retrospective cohort study of ischemic stroke patients with and without COPD in the Geisinger Neuroscience Ischemic Stroke database to examine all-cause mortality up to 3 years using Kaplan-Meier estimator and Cox proportional hazards model. ResultsOf the 6,589 ischemic stroke patients included in this study, 5,525 (83.9%) did not have COPD (group A). Group B (n=1,006) consisted of patients with COPD diagnosis by ICD-9/10-CM codes. COPD patients in Group C (n=233) were diagnosed by spirometry, and in Group D (n=175) by both ICD-9/10-CM codes and spirometry confirmation. The survival probabilities at three years in Group B, C, and D were significantly lower than in Group A. Group B (HR=1.262, 95% CI 1.122–1.42, p<0.001) and group C (HR=1.251, 95% CI 1.01–1.55, p=0.041) had significantly lower hazard of mortality compared to group A. There was no significant difference in survival between COPD subtypes of chronic bronchitis and emphysema. Patients in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 stage had an increased mortality hazard compared to the GOLD 1 stage. ConclusionsWhile ischemic stroke patients with preexisting COPD have worse long-term survival than those without COPD, the results largely depended on the definition of COPD used. These results suggest that ischemic stroke patients with COPD need more personalized medical care to decrease long-term mortality.

Radiographic Phenotypes Affect the Risk of Inhaled Corticosteroid-Associated Pneumonia in Patients with COPD.

PurposeFew studies have reported the association between the radiographic characteristics and the development of pneumonia in patients with chronic obstructive pulmonary disease (COPD) treated with inhaled corticosteroids (ICSs). Our study aimed to assess the effect of radiographic phenotypes on the risk of pneumonia in patients treated with ICSs.Patients and MethodsThis study retrospectively analysed all patients with COPD treated with ICSs in a subset of the Korea Chronic Obstructive Pulmonary Disorders Subgroup Study registry between January 2017 and December 2019. The association between radiographic phenotypes including the presence and severity of emphysema, airway wall thickening, or bronchiectasis on chest computed tomography were determined visually/qualitatively and the risk of pneumonia was analyzed using the Cox regression model.ResultsAmong the 90 patients with COPD treated with ICSs, 41 experienced pneumonia more than once during the median follow-up of 29 (interquartile range, 8–35) months. In univariate Cox regression analysis, older age, longer use of ICSs, use of fluticasone propionate or metered dose inhaler, and severe exacerbation events increased the risk of pneumonia. In multivariate analysis, the presence of emphysema (adjusted hazard ratio [aHR]=3.73, P=0.033), severity measured using the visual sum score (mild-to-moderate, aHR=8.58, P=0.016; severe, aHR=3.58, P=0.042), Goddard sum score (mild-to-moderate, aHR=3.31, P=0.058; severe, aHR=5.38, P=0.014), and the upper lobe distribution of emphysema (aHR=3.76, P=0.032) were associated with a higher risk of pneumonia. Subtypes of centrilobular and panlobular emphysema had a higher risk of pneumonia compared with paraseptal emphysema (aHR=3.98, P=0.033; HR=3.91, P=0.041 vs HR=2.74, P=0.304). The presence of bronchiectasis (aHR=2.41, P=0.02) and emphysema/bronchiectasis overlap phenotype (aHR=2.19, P=0.053) on chest CT was a risk factor for pneumonia in this population. However, severity of bronchiectasis and the presence or severity of bronchial wall thickening according to the visual sum score were not associated with the risk of pneumonia.ConclusionAmong patients with COPD treated with ICSs, radiographic phenotypes including the presence of emphysema, bronchiectasis or emphysema/bronchiectasis overlap phenotype, severity with emphysema, subtypes of centrilobular or panlobular emphysema, and upper lobe distribution of emphysema may help predict the risk of pneumonia.

Association between Chest CT–defined Emphysema and Lung Cancer: A Systematic Review and Meta-Analysis

Background Given the different methods of assessing emphysema, controversy exists as to whether it is associated with lung cancer. Purpose To perform a systematic review and meta-analysis of the association between chest CT-defined emphysema and the presence of lung cancer. Materials and Methods The PubMed, Embase, and Cochrane databases were searched up to July 15, 2021, to identify studies on the association between emphysema assessed visually or quantitatively with CT and lung cancer. Associations were determined by emphysema severity (trace, mild, or moderate to severe, assessed visually and quantitatively) and subtype (centrilobular and paraseptal, assessed visually). Overall and stratified pooled odds ratios (ORs) with their 95% CIs were obtained. Results Of the 3343 screened studies, 21 studies (107 082 patients) with 26 subsets were included. The overall pooled ORs for lung cancer given the presence of emphysema were 2.3 (95% CI: 2.0, 2.6; I2 = 35%; 19 subsets) and 1.02 (95% CI: 1.01, 1.02; six subsets) per 1% increase in low attenuation area. Studies with visual (pooled OR, 2.3; 95% CI: 1.9, 2.6; I2 = 48%; 12 subsets) and quantitative (pooled OR, 2.2; 95% CI: 1.8, 2.8; I2 = 3.7%; eight subsets) assessments yielded comparable results for the dichotomous assessment. Based on six studies (1716 patients), the pooled ORs for lung cancer increased with emphysema severity and were higher for visual assessment (2.5, 3.7, and 4.5 for trace, mild, and moderate to severe, respectively) than for quantitative assessment (1.9, 2.2, and 2.5) based on point estimates. Compared with no emphysema, only centrilobular emphysema (three studies) was associated with lung cancer (pooled OR, 2.2; 95% CI: 1.5, 3.2; P < .001). Conclusion Both visual and quantitative CT assessments of emphysema were associated with a higher odds of lung cancer, which also increased with emphysema severity. Regarding subtype, only centrilobular emphysema was significantly associated with lung cancer. Clinical trial registration no. CRD42021262163 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hunsaker in this issue.

The prevalence and physiological impacts of centrilobular and paraseptal emphysema on computed tomography in smokers with preserved ratio impaired spirometry.

Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are observed in smokers with preserved ratio impaired spirometry (PRISm, defined as the ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) ≥0.7 and FEV1 <80%), but their prevalence and physiological impacts remain unestablished. This multicentre study aimed to investigate its prevalence and to test whether emphysema subtypes are differently associated with physiological impairments in smokers with PRISm.Both never- and ever-smokers aged ≥40 years who underwent computed tomography (CT) for lung cancer screening and spirometry were retrospectively and consecutively enrolled at three hospitals and a clinic. Emphysema subtypes were visually classified according to the Fleischner system. Air-trapping was assessed as the ratio of FVC to total lung capacity on CT (TLCCT).In 1046 never-smokers and 772 smokers with ≥10 pack-years, the prevalence of PRISm was 8.2% and 11.3%, respectively. The prevalence of PSE and CLE in smokers with PRISm was comparable to that in smokers with normal spirometry (PSE 43.7% versus 36.2%, p=1.00; CLE 46.0% versus 31.8%, p=0.21), but higher than that in never-smokers with PRISm (PSE 43.7% versus 1.2%, p<0.01; CLE 46% versus 4.7%, p<0.01) and lower than that in smokers with airflow limitation (PSE 43.7% versus 71.0%, p<0.01; CLE 46% versus 79.3%, p<0.01). The presence of CLE, but not PSE, was independently associated with reduced FVC/TLCCT in smokers with PRISm.Both PSE and CLE were common, but only CLE was associated with air-trapping in smokers with PRISm, suggesting different physiological roles of these emphysema subtypes.

Impact of Emphysema Subtypes and Volume on Lung Ventilation and Gas Exchange Functions as Evidenced by Computed Tomography

Objective: to characterize the relationship between the subtype and volume of pulmonary emphysema on the indicators of lung ventilation and gas exchange functions. Material and methods. The data of radiation and functional studies were analyzed in 50 patients. The inclusion criteria were chronic obstructive pulmonary disease and emphysema, which had been diagnosed by computed tomography (CT) and confirmed by two radiologists; comprehensive pulmonary function studies, including spirometry and body plethysmography, were performed; diffusion capacity was measured using a single-breath method, involving inhalation of carbon monoxide, and a breath hold. Patients with primary pulmonary emphysema, any history of pulmonary surgery, and emphysema concurrent with other lung X-ray syndromes (consolidation, cavity) were excluded. CT was performed with a 1-mm thick slice and standard scanning parameters on Toshiba tomographs (Japan). Pulmonary function was tested using a MasterScreen Body Diffusion expert diagnostic unit (VIASYS Healthcare, Germany) in accordance with the criteria for correct pulmonary functional tests proposed by a joint group of experts from the American Thoracic Society and the European Respiratory Society. Volumetric analysis of emphysema was performed using the Lung Volume Analysis software package (Toshiba, Japan). In the study, there was a predominance of male patients (n = 42 (84%)), mainly in the 61-70 age group. Results. The isolated type of emphysema was rare: centrilobular and paraseptal emphysemas were seen in 3 (6%) and 2 (4%) patients, respectively. The mixed type of emphysema was detected in 90% of cases; 33 (66%) patients having a predominant centrilobular component constituted a large proportion. It was determined that as the volume of emphysema increased, the patency of the airways worsened, the static pulmonary volumes increased, the lungs were hyperinflated, pulmonary gas exchange worsened, the bronchial resistance slightly increased during calm breathing. No statistically significant results were found from the point of view of correlations between the volume of emphysema and other parameters of pulmonary function. Conclusion. An increase in the volume of emphysema deteriorates pulmonary function; the greatest contribution to the overall picture is made by the patients with a mixed type of emphysema with a predominance of the centrilobular component.

A vision transformer for emphysema classification using CT images

Objective. Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation. Approach. We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61 61) are cropped from CT images which contain the area of normal lung parenchyma, CLE, PLE, and PSE. After resizing, the large patch is divided into small patches and these small patches are converted to a sequence of patch embeddings by flattening and linear embedding. A class embedding is concatenated to the patch embedding, and the positional embedding is added to the resulting embeddings described above. Then, the obtained embedding is fed into the transformer encoder blocks to generate the final representation. Finally, the learnable class embedding is fed to a softmax layer to classify the emphysema. Main results. To overcome the lack of massive data, the transformer encoder blocks (pre-trained on ImageNet) are transferred and fine-tuned in our ViT model. The average accuracy of the pre-trained ViT model achieves 95.95% in our lab’s own dataset, which is higher than that of AlexNet, Inception-V3, MobileNet-V2, ResNet34, and ResNet50. Meanwhile, the pre-trained ViT model outperforms the ViT model without the pre-training. The accuracy of our pre-trained ViT model is higher than or comparable to that by available methods for the public dataset. Significance. The results demonstrated that the proposed ViT model can accurately classify the subtypes of emphysema using CT images. The ViT model can help make an effective computer-aided diagnosis of emphysema, and the ViT method can be extended to other medical applications.

Panlobular emphysema is associated with COPD disease severity: A study of emphysema subtype by computed tomography

BackgroundComputed tomography has the potential to inform COPD prognosis. We sought to determine associations of emphysema phenotype with clinical parameters including lung function, inflammatory markers, and quality of life. MethodsParticipants of this single-center observational cohort (n = 83) were 40–80 years old, had ≥10 pack-year smoking, and a diagnosis of COPD confirmed by spirometry. All participants had available historic chest CT scans which were systematically reviewed by a single expert radiologist and scored for emphysema subtype, extent, and distribution. Associations between radiographic findings and clinical parameters were determined. ResultsMedian age of participants was 72 years, median smoking 40 pack-years, and median FEV1 59% predicted. 84% of the participants had radiographic emphysema. Of those, 26% had panlobular emphysema (PLE), 68% centrilobular emphysema (CLE), and 6% paraseptal emphysema (PSE). As compared to the participants with no radiographic emphysema, the presence of PLE-dominant emphysema was associated with a lower BMI (P = 0.012) and greater extent of emphysema (P = 0.014). After adjusting for age, sex, and pack-years smoking history, PLE was associated with greater airflow obstruction by FEV1% (48% vs 71%, P = 0.005), greater symptom burden by CAT score (18 vs 9, P = 0.015), worse quality of life by SGRQ score (43 vs 22, P = 0.025), and more systemic inflammation by erythrocyte sedimentation rate (P = 0.001). CLE- or PSE-dominant emphysema were not similarly associated with clinical features or symptom burden. ConclusionsThe presence of PLE-dominant emphysema was associated with greater extent of emphysema, greater airflow obstruction, increased respiratory symptoms, worse quality of life, and systemic inflammation. Further investigation is indicated to explore the pathogenesis of the PLE phenotype and the prognostic and treatment implications of PLE.

Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.

Recent advances in airway imaging using micro-computed tomography and computed tomography for chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a complex lung disease characterized by a combination of airway disease and emphysema. Emphysema is classified as centrilobular emphysema (CLE), paraseptal emphysema (PSE), or panlobular emphysema (PLE), and airway disease extends from the respiratory, terminal, and preterminal bronchioles to the central segmental airways. Although clinical computed tomography (CT) cannot be used to visualize the small airways, micro-CT has shown that terminal bronchiole disease is more severe in CLE than in PSE and PLE, and micro-CT findings suggest that the loss and luminal narrowing of terminal bronchioles is an early pathological change in CLE. Furthermore, the introduction of ultra-high-resolution CT has enabled direct evaluation of the proximal small (1 to 2-mm diameter) airways, and new CT analytical methods have enabled estimation of small airway disease and prediction of future COPD onset and lung function decline in smokers with and without COPD. This review discusses the literature on micro-CT and the technical advancements in clinical CT analysis for COPD. Hopefully, novel micro-CT findings will improve our understanding of the distinct pathogeneses of the emphysema subtypes to enable exploration of new therapeutic targets, and sophisticated CT imaging methods will be integrated into clinical practice to achieve more personalized management.