Inspiratory Chest Research Articles

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD.

Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.

Detection of chronic obstructive pulmonary disease with deep learning using inspiratory and expiratory chest computed tomography and clinical information.

In recent years, more and more patients with chronic obstructive pulmonary disease (COPD) have remained undiagnosed despite having undergone medical examination. This study aimed to develop a convolutional neural network (CNN) model for automatically detecting COPD using double-phase (inspiratory and expiratory) chest computed tomography (CT) images and clinical information. A total of 2,047 participants, including never-smokers, ex-smokers, and current smokers, were prospectively recruited from three hospitals. The double-phase CT images and clinical information of each participant were collected for training the proposed CNN model which integrated a sequence of residual feature extracting blocks network (RFEBNet) for extracting CT image features and a fully connected feed-forward network (FCNet) for extracting clinical features. In addition, the RFEBNet utilizing double- or single-phase CT images and the FCNet using clinical information were conducted for comparison. The proposed CNN model, which utilized double-phase CT images and clinical information, outperformed other models in detecting COPD with an area under the receiver operating characteristic curve (AUC) of 0.930 [95% confidence interval (CI): 0.913-0.951] on an internal test set (n=307). The AUC was higher than the RFEBNet using double-phase CT images (AUC =0.912, 95% CI: 0.891-0.932), single inspiratory CT images (AUC =0.888, 95% CI: 0.863-0.915), single expiratory CT images (AUC =0.897, 95% CI: 0.874-0.925), and FCNet using clinical information (AUC =0.805, 95% CI: 0.777-0.841). The proposed model also achieved the best performance on an external test (n=516) with an AUC of 0.896 (95% CI: 0.871-0.931). The proposed CNN model using double-phase CT images and clinical information can automatically detect COPD with high accuracy.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant etal. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n=3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n=80) and validation (n=40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n=236) was evaluated. The vertebral segmentation module achieved a Dice score of.984 as compared to manual outlining results as reference (n=100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being.980 and.989, respectively. The vertebral labelling module achieved 100% accuracy (n=100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of.995 and.999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n=236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography.

Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL. From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT). The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively. We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the lung radiomics and 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 of accuracy, 90.9 of precision, 89.5 of F1-score, and 95.8 of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.

Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry. We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD. Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001). Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Comparison of Diaphragmatic Stretch and Manual Diaphragmatic Release Technique Along with Chest Proprioceptive Neuromuscular Facilitation in Mild-to-moderate Chronic Obstructive Pulmonary Disease: A Randomized Clinical Trial

Background: Diaphragmatic stretch technique, manual diaphragmatic release technique, and chest proprioceptive neuromuscular facilitation (PNF) have been suggested as an alternative method for the treatment of chronic obstructive pulmonary disease (COPD). Aims: The aim of this study was to compare the effects of diaphragmatic stretch and manual diaphragmatic release technique along with chest PNF on peak expiratory flow rate (PEFR), exercise capacity, dyspnea, chest expansion, and inspiratory pressure in mild-to-moderate COPD participants. Settings and Design: A randomized clinical trial took place on 30 subjects with COPD. Materials and Methods: Subjects were divided into two groups, of which Group A received a diaphragmatic stretch technique with chest PNF and Group B received a manual diaphragmatic release technique with chest PNF. Six-day intervention protocol was given to the participants of both the groups and PEFR, maximum inspiratory pressure, chest expansion, exercise capacity, and dyspnea were assessed before the intervention and after 6 days of intervention. Statistical Analysis: Normality of all continuous data was checked using the Kolmogorov–Smirnov test. Independent t-test was used to analyze the parameters between Group A and Group B. Comparison of difference in pre and post within group was done using independent t-test. Results: Data analyzed in Group A as well as in B, when compared within the group, showed highly significant improvements. When compared between the groups, both the groups showed no difference. Conclusion: The study concluded that there was statistical significance in improvement of PEFR, exercise capacity, dyspnea, chest expansion, and inspiratory pressure within both the groups, but significant changes were not seen between groups. Hence, diaphragmatic stretch and manual release technique along with chest PNF can be used as an alternative/adjunct for the treatment of COPD.

Predicted versus CT-derived total lung volume in a general population: The ImaLife study.

Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45-65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.

Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic Obstructive Pulmonary Disease.

Disease probability measure (DPM) is a useful voxel-wise imaging assessment of gas-trapping and emphysematous lesions in patients with chronic obstructive pulmonary disease (COPD). To elucidate the progression of COPD, we performed a cluster analysis using the following DPM parameters: normal (DPMNormal), gas-trapping (DPMGasTrap), and emphysematous lesions (DPMEmph). Our findings revealed the characteristics of each cluster and the 3-year disease progression using imaging parameters. Inspiratory and expiratory chest computed tomography (CT) images of 131 patients with COPD were examined, of which 84 were followed up for 3 years. The percentage of low attenuation volume (LAV%) and the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) were quantitatively measured using inspiratory chest CT. A hierarchical cluster analysis was performed using the DPM parameters at baseline. Five clusters were named according to the dominant DPM parameters: normal (NL), normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM), and Emphysema (EM). Women were predominantly diagnosed with GT. Forced expiratory volume in 1 s gradually decreased in the following order: NL, NL-GT, GT, GT-EM, and EM. DPMEmph correlated well with LAV%. Four clusters other than NL showed significantly higher values of √Aaw at Pi10 than NL; however, no significant differences were observed among them. In all clusters, DPMEmph increased after 3 years. DPMNormal only increased in the GT cluster. Clusters using DPM parameters may reflect the characteristics of COPD and help understand the pathophysiology of the disease.

Breathing and Light- to Vigorous-Intensity Aerobic Exercises Improved Respiratory Functions and Functional Capacity of COVID-19 Survivor with Morbid Obesity

Introduction: A decrease in respiratory functions (RF) and functional capacity (FC) may present as complications of intensive care unit (ICU) admission. Morbid obesity worsens these complications. Case: A 31-year-old male patient with morbid obesity came for pulmonary rehabilitation (PR) one week after hospitalization. He suffered from COVID-19 and received 15 days of mechanical ventilation. The goal of PR was to improve RF and FC. A comprehensive PR, including hospital- and home-based programs, consisting of breathing, aerobic, resistance, and flexibility exercises, diet and psychological counseling was given. Breathing exercises were deep breathing, sustained-maximal breathing, and chest expansion. Hospital-based PR was given with moderate-intensity interval aerobic exercise (AE), while home-based PR was with low-intensity continuous, both with oxygen supplementation. The FC was needed to do his daily activities, and vocational was 1.0 to 6.3 metabolic equivalents (METs). The target of FC 6 METs in 12-18 weeks was set. After six weeks, the RF improved with decreased dyspnea and increased maximum inspiratory volume and chest expansion. The FC increased to 4.2 METs, and monitored-home-based AE was given with vigorous-intensity interval mode. He joined the residency program 10 weeks later and achieved 5.7 METs at the end of PR. We gave unsupervised home-based exercises for his long-term exercise. Conclusion: Breathing and AE improved RF and FC in a COVID-19 patient with morbid obesity admitted to the intensive care unit (ICU) to previous vocational activities.

COMPARISON AND CORRELATION OF CARDIOTHORACIC RATIO ON CHEST RADIOGRAPH AND COMPUTED TOMOGRAPHY

Chest radiograph is an easily available non invasive tool in evaluation of health. The cardiothoracic ratio (CTR) is commonly used as an indicator of cardiomegaly in clinical radiology. It can promptly diagnose congestive cardiomegaly apart from other lung and pleural pathologies and also depicts repsonse to treatment given, like diuretics in heart failure. Although there are better modalities available like echocardiography, computed tomography and magentic resonance imaging, chest radiography and cardiothoracic ratio are still being used by clinicians, especially in emergency department, intensive care unit and for frequent follow ups . CTR is calculated by dividing the cardiac diameter (CD) by the thoracic diameter (TD) as measured on posteroanterior chest radiography (chest PA). CTR of &gt;0.5 is abnormal on an inspiratory breath-hold chest radiograph. The purpose of this study was to compare the statistical correlation of CTR between chest radiograph with CT and to present real Heart Size (HS), Thoracic Size (TS), and Cardiothoracic Ratio (CTR) on chest PA and chest AP in the DR system and in computed tomography (CT). The conclusion of the study is that the measurement of CTR from radiograph &amp; computed tomography (CT) from both axial &amp; coronal reformations were concordant with another

Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia

PurposeTo assess feasibility of automated segmentation and measurement of tracheal collapsibility for detecting tracheomalacia on inspiratory and expiratory chest CT images. MethodsOur study included 123 patients (age 67 ± 11 years; female: male 69:54) who underwent clinically indicated chest CT examinations in both inspiration and expiration phases. A thoracic radiologist measured anteroposterior length of trachea in inspiration and expiration phase image at the level of maximum collapsibility or aortic arch (in absence of luminal change). Separately, another investigator separately processed the inspiratory and expiratory DICOM CT images with Airway Segmentation component of a commercial COPD software (IntelliSpace Portal, Philips Healthcare). Upon segmentation, the software automatically estimated average lumen diameter (in mm) and lumen area (sq.mm) both along the entire length of trachea and at the level of aortic arch. Data were analyzed with independent t-tests and area under the receiver operating characteristic curve (AUC). ResultsOf the 123 patients, 48 patients had tracheomalacia and 75 patients did not. Ratios of inspiration to expiration phases average lumen area and lumen diameter from the length of trachea had the highest AUC of 0.93 (95% CI = 0.88-0.97) for differentiating presence and absence of tracheomalacia. A decrease of ≥25% in average lumen diameter had sensitivity of 82% and specificity of 87% for detecting tracheomalacia. A decrease of ≥40% in the average lumen area had sensitivity and specificity of 86% for detecting tracheomalacia. ConclusionAutomatic segmentation and measurement of tracheal dimension over the entire tracheal length is more accurate than a single-level measurement for detecting tracheomalacia.

Effect of Upright and Slouched Postures on the Diaphragm Strength and Chest Expansion in Obese Young Adults – An Observational Study

Context: Obese individuals are at high risk of adopting slouched posture because of their excessive body fat distribution. Moreover, excess body fat limits the action of the respiratory muscles and restricts diaphragm mobility and rib movement. Aims: The aim of this study was to study the influence of upright versus slouched posture on respiratory muscle strength and chest expansion in obese young adults. Settings and Design: The participants included in the study were young adults of all genders between the age group of 18 and 25 years with a body mass index above 27.5. Methods and Materials: It is an observational study. Thirty participants were recruited from health-care institutions. Screening was done according to the inclusion and exclusion criteria. Diaphragm strength and chest expansion were assessed in upright sitting and slouched sitting postures. Statistical analysis used: SPSS statistical package version 23 was used to analyze the data. Normality of the baseline data was checked using Student's t-test. To summarize the data, descriptive statistics (mean and standard deviation) were employed. To discover variations in maximal inspiratory pressure (MIP) scores and chest expansion between slouched and upright sitting positions, a paired t-test analysis was performed. Results: The participants showed a lower MIP score in slouched sitting as compared to an upright sitting posture. The mean difference in the MIP scores was 20.99 mmHg (P = 0.04). Chest expansion measurements also showed a significant difference between the two sitting postures. Conclusions: The lower maximal inspiratory pressure and chest expansion measures observed in this study indicate that in obese individuals, slouched sitting posture can adversely affect the diaphragm strength and the chest wall mechanics during breathing.

Dyspnoea – a valuable clue in a rare disease diagnosis

Abstract Pulmonary veno-occlusive disease (PVOD) is a rare microvasculopathy associated with dyspnoea, pulmonary arterial hypertension (PAH) and poor prognosis. A 42-year-old female, with a known history of cured thyroid neoplasm, complained of severe inspiratory progressive dyspnoea, chest pain, dry cough and asthenia. The initial investigations established the diagnosis of idiopathic PAH. Specific associated vasodilator treatment was initiated but with sildenafil intolerance. The clinical condition continued deteriorating, and thus the investigations were extended. DLCO was unnaturally low (24%). Angiography detected a minimal left distal CTEPH (not suitable for balloon angioplasty). Genetic test came positive for biallelic mutations of EIF2AK4, suggestive for PVOD. Finally, the diagnostic was changed to PAH in the PVOD context. A decision was made to refer the patient for lung transplant and to associate treprostinil treatment (with slight clinical improvement). Therefore, severe dyspnoea, hypoxia, decreased DLCO and poor response to vasodilator treatment compel physicians to search for PVOD and refer to lung transplant.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

Evaluation of Gas Trapping in Chronic Obstructive Pulmonary Disease: Prediction of Parametric Response Mapping from Solo Inspiratory Chest CT Scan by Deep Learning

Evaluation of Gas Trapping in Chronic Obstructive Pulmonary Disease: Prediction of Parametric Response Mapping from Solo Inspiratory Chest CT Scan by Deep Learning

Inspiratory and Expiratory Chest High-resolution CT: Small-airway Disease Evaluation in Patients with COVID-19.

An outbreak of coronavirus disease 2019 (COVID-19) has occurred worldwide. However, the small-airway disease in patients with COVID-19 has not been explored. This study aimed to explore the small-airway disease in patients with COVID-19 using inspiratory and expiratory chest high-resolution computed tomography (CT). This multicenter study included 108 patients with COVID-19. The patients were classified into five stages (0-IV) based on the CT images. The clinical and imaging data were compared among CT images in different stages. Patients were divided into three groups according to the time interval from the initial CT scan, and the clinical and air trapping data were compared among these groups. The correlation between clinical parameters and CT scores was evaluated. The clinical data, including age, frequency of breath shortness and dyspnea, neutrophil percentage, lymphocyte count, PaO2, PaCO2, SaO2, and time interval between the onset of illness and initial CT, showed significant differences among CT images in different stages. A significant difference in the CT score of air trapping was observed between stage I and stage III. A low negative correlation was found between the CT score of air trapping and the time interval between the onset of symptoms and initial CT. No significant difference was noted in the frequency and CT score of air trapping among different groups. Some patients with COVID-19 developed small-airway disease. Air trapping was more distinguished in the early stage of the disease and persisted during the 2-month follow-up. Longer-term follow-up studies are needed to confirm the findings.

Unexpected Cause of Pericarditis: Chest Teratoma

Mediastinal teratoma rarely causes pericarditis. We report a case of a 22-year-old young female admitted to the emergency department for inspiratory chest pain and fever with severe pericardial effusion, unexepectable the cause of pericarditis was a mediastinal teratoma.

The Enhanced Value of Parameter Response Mapping in a One-Stop CT Scanning: Predicting Pulmonary Function Test with Machine Learning

Background: To evaluate the performance of a machine learning approach to predict pulmonary function test (PFT) result from parameter response mapping (PRM), which enhances the value of one-stop CT scanning. Methods: 615 subjects with PFT and paired inspiratory and expiratory chest CT scanning were enrolled retrospectively, and classified into normal group, high risk group and COPD group based on PFT. 72 PRM-derived quantitative parameters including volume (cc) and volume percentage (%) of emphysema, functional-small airways disease (fSAD), and normal lung tissue (Normal) were acquired. Random forest regression model was constructed and tested on the aspect of PFT prediction, and the classification performance based on these PFT predictions was further evaluated. Findings: The machine-learning model based on PRM parameters showed good performance to predict PFT, with a coefficient of determination (R2) of 0·749 and 0·792 for FEV1/FVC and FEV1% regression respectively. The sensitivity, specificity, and accuracy for differentiating normal group from high-risk group were 0·85, 0·90 and 0·88, respectively; and 0·89, 1 and 0·99, respectively, for differentiating non-COPD group from COPD group. Interpretation: A machine learning model based on CT-derived PRM parameters could enhance the clinical value of one-stop chest CT scanning by predicting PFT results. The technique has the potential to benefit patients in clinical practice, especially for those with difficulty to perform PFT. Funding Statement: This work was supported by the National Natural Science Foundation of China [grant number 81871321, 81930049]; the National Key R&D Program of China [grant number 2016YFE0103000, 2017YFC1308703]. Declaration of Interests: None. Ethics Approval Statement: This study was approved by the hospital ethics committee and the informed consent of patients was waived.