Chest Computed Tomography Analysis Research Articles

Subchronic particulate matter exposure underlying polyhexamethylene guanidine phosphate–induced lung injury: Quantitative and qualitative evaluation with chest computed tomography

Our study was to explore the effects of subchronic particulate matter (PM) exposure on lung injury induced by polyhexamethylene guanidine phosphate (PHMG-p) in a rat model. Specifically, we investigated pulmonary inflammation, fibrosis, and tumor formation using chest computed tomography (CT), and histopathologic examination. PHMG-p was administered intratracheally to 20 male rats. After an initial week of PHMG-p treatment, the experimental group (PM group) received intratracheal administration of PM suspension, while the control group received normal saline. This regimen was continued for 10 weeks to induce subchronic PM exposure. Chest CT scans were conducted on all rats, followed by the extraction of both lungs for histopathological analysis. All CT images underwent comprehensive quantitative and qualitative analyses. Pulmonary inflammation was markedly intensified in rats subjected to subchronic PM exposure in the PM group compared to those in the control. Similarly, lung fibrosis was more severe in the PM group as observed on both chest CT and histopathologic examination. Quantitative chest CT analysis revealed that the mean lesion volume was significantly greater in the PM group than in the control group. Although the incidence of bronchiolo-alveolar hyperplasia was higher in the PM group compared to the control group, this difference was not statistically significant. In summary, subchronic PM exposure exacerbated pulmonary inflammation and fibrosis underlying lung injury induced by PHMG-p.

Advancements in imaging in ChILD.

Interstitial and diffuse lung diseases in children constitute a range of congenital and acquired disorders. These disorders present with signs and symptoms of respiratory disease accompanied by diffuse radiographic changes. In many cases, radiographic findings are nonspecific, while in other disorders, chest computed tomography (CT) is diagnostic in the appropriate context. Regardless, chest imaging remains central in the evaluation of the patient with suspected childhood interstitial lung disease(chILD). Several newly described chILD entities, spanning both genetic and acquired etiologies, have imaging that aid in their diagnoses. Advances in CT scanning technology and CT analysis techniques continue to improve scan quality as well as expand use of chest CT as a research tool. Finally, ongoing research is expanding use of imaging modalities without ionizing radiation. Magnetic resonance imaging is being applied to investigate pulmonary structure and function, and ultrasound of the lung and pleura is a novel technique with an emerging role in chILD disorders. This review describes the current state of imaging in chILD including recently described diagnoses, advances in conventional imaging techniques and applications, and evolving new imaging modalities that expand the clinical and research roles for imaging in these disorders.

The effect of CFTR modulators on structural lung disease in cystic fibrosis.

Background: Newly developed quantitative chest computed tomography (CT) outcomes designed specifically to assess structural abnormalities related to cystic fibrosis (CF) lung disease are now available. CFTR modulators potentially can reduce some structural lung abnormalities. We aimed to investigate the effect of CFTR modulators on structural lung disease progression using different quantitative CT analysis methods specific for people with CF (PwCF). Methods: PwCF with a gating mutation (Ivacaftor) or two Phe508del alleles (lumacaftor-ivacaftor) provided clinical data and underwent chest CT scans. Chest CTs were performed before and after initiation of CFTR modulator treatment. Structural lung abnormalities on CT were assessed using the Perth Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF), airway-artery dimensions (AA), and CF-CT methods. Lung disease progression (0-3years) in exposed and matched unexposed subjects was compared using analysis of covariance. To investigate the effect of treatment in early lung disease, subgroup analyses were performed on data of children and adolescents aged <18years. Results: We included 16 modulator exposed PwCF and 25 unexposed PwCF. Median (range) age at the baseline visit was 12.55 (4.25-36.49) years and 8.34 (3.47-38.29) years, respectively. The change in PRAGMA-CF %Airway disease (-2.88 (-4.46, -1.30), p = 0.001) and %Bronchiectasis extent (-2.07 (-3.13, -1.02), p < 0.001) improved in exposed PwCF compared to unexposed. Subgroup analysis of paediatric data showed that only PRAGMA-CF %Bronchiectasis (-0.88 (-1.70, -0.07), p = 0.035) improved in exposed PwCF compared to unexposed. Conclusion: In this preliminary real-life retrospective study CFTR modulators improve several quantitative CT outcomes. A follow-up study with a large cohort and standardization of CT scanning is needed to confirm our findings.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BackgroundThe role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.MethodsLungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.ResultsDespite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.ConclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.Key pointsWe conducted a multicenter evaluation of the deep learning-based LungQuant automated software.We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions.Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations.An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Imaging Regional Airway Involvement of Asthma: Heterogeneity in Ventilation, Mucus Plugs and Remodeling.

The imaging of asthma using chest computed tomography (CT) is well-established (Jarjour et al., Am J Respir Crit Care Med 185(4):356-62, 2012; Castro et al., J Allergy Clin Immunol 128:467-78, 2011). Moreover, recent advances in functional imaging of the lungs with advanced computer analysis of both CT and magnetic resonance images (MRI) of the lungs have begun to play a role in quantifying regional obstruction. Specifically, quantitative measurements of the airways for bronchial wall thickening, luminal narrowing and distortion, the amount of mucus plugging, parenchymal density, and ventilation defects that could contribute to the patient's disease course are instructive for the entire care team. In this chapter, we will review common imaging methods and findings that relate to the heterogeneity of asthma. This information can help to guide treatment decisions. We will discuss mucous plugging, quantitative assessment of bronchial wall thickening, delta lumen phenomenon, parenchymal low-density lung on CT, and ventilation defect percentage on MRI as metrics for assessing regional ventilatory dysfunction.

Computerised quantification of lung infiltration in chest computed tomography of COVID-19 patients and its correlation with survival outcome

Introduction: COVID-19 is a newly emerged global pandemic since the end of 2019. High-resolution chest computed tomography (CT) is the main imaging modality for identification of the severity of lung infiltrations and consolidations at the time of admission. Tools for quantitative chest CT analysis are rare and its effectiveness as a predictor for COVID-19 survival has not yet been demonstrated. This study aimed to predict the survival outcome for COVID-19 patients in Gaza Strip, Palestine, based on the semi-automated, free, and open-source Lung CT Analyzer tool (LCTA).

CT imaging findings in lung transplant recipients with COVID-19.

ObjectivesOur goal was to compare the chest computed tomography (CT) imaging findings of COVID-19 in lung transplant recipients (LTR) and a group of non-transplanted controls (NTC).MethodsThis retrospective study included 51 consecutive LTR hospitalized with COVID-19 from two centers. A total of 75 NTC were included for comparison. Images were classified regarding the standardized RSNA category, main pattern of lung attenuation, and longitudinal and axial distribution. Quantitative CT (QCT) analysis was performed to evaluate percentage of high attenuation areas (%HAA, threshold −250 to −700 HU). CT scoring was used to measure severity of parenchymal abnormalities.ResultsThe imaging findings of COVID-19 in LTR were significantly different from controls regarding the RSNA classification and pattern of lung attenuation. LTR had a significantly higher proportion of patients with an indeterminate pattern on CT (0.31 vs. 0.11, p = 0.014). The most frequent pattern of attenuation in LTR was predominantly consolidation (0.39 vs. 0.22, p = 0.144) followed by a mixed pattern of ground-glass opacities (GGO) and consolidation (0.37 vs. 0.20, adjusted p = 0.102). On the other hand, the most common pattern in NTC was GGO predominant (0.58 vs. 0.24 of LTR, p = 0.001). LTR had significantly more severe parenchymal disease measured by CT score and %HAA by QCT (0.372 ± 0.08 vs. 0.148 ± 0.06, p < 0.001).ConclusionThe most frequent finding of COVID-19 in LTR is a predominant pattern of consolidation. Compared to NTC, LTR more frequently demonstrated an indeterminate pattern according to the RSNA classification and more extensive lung abnormalities on QCT and semi-quantitative scoring.Key Points• The most common CT finding of COVID-19 in LTR is a predominant pattern of consolidation followed by a mixed pattern of GGO and consolidation, while controls more often have a predominant pattern of GGO.• LTR more often presents with an indeterminate pattern of COVID-19 by RSNA classification than controls; therefore, molecular testing for COVID-19 is essential for LTR presenting with lower airway infection independently of imaging findings.• LTR had more extensive disease by semi-quantitative CT score and increased percentage areas of high attenuation on QCT.

AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs.

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images.

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Comparison of the Main Staging Systems for Assessing the Severity of Lung Injury in Patients with COVID-19 and Evaluation of Their Predictive Value

Objective: to compare the results of staging the severity of viral pneumonia in patients with COVID-19 based on the results of chest computed tomography (CT) using the empirical visual scale CT 0–4 and chest CT severity score (CT-SS) point scale, as well as to assess their prognostic value.Material and methods. Chest CT scans and anamnestic data in patients hospitalized to a non-specialized center repurposed for the treatment of new coronavirus infection, were analyzed. Chest CT analysis was performed by two radiologists using CT 0–4 and CT-SS scales.Results. The time course of changes in the severity of lung parenchymal lesions, by using both scales, was found to be similar: the maximum magnitude of lung tissue changes was recorded on day 5 of the disease. In cases of death, there was a significantly more extensive lung parenchymal involvement at admission to the center than in recovered patients, which was also true for both CT data assessment systems. Bothscales demonstrated comparable diagnostic and prognostic value: there were no statistically significant differences in sensitivity, specificity, and predictive value of a fatal outcome. Both the CT 0–4 scales and the CT-SS are based on the estimation of the volume of the affected lung tissue, but when the CT 0–4 scale was employed, additional criteria were used in some cases: the presence of hydrothorax and the determination of the maximum score for the most affected lung. Not all patients with a pronounced CT picture of viral pneumonia had a fatal outcome, which may indicate the presence of other factors that increase its risk.Conclusion. Both CT 0–4 and CT-SS scales have similar predictive values. The greater severity of parenchymal damage assessed by these CT scales was associated with the higher mortality rate.

Semiquantitative Chest CT Severity Score Predicts Failure of Noninvasive Positive-Pressure Ventilation in Patients Hospitalized for COVID-19 Pneumonia

ObjectiveNoninvasive positive-pressure ventilation (NPPV) emerged as an efficient tool for treatment of COVID-19 pneumonia. The factors influencing NPPV failure still are elusive. The aim of the study was to investigate the relationships between semiquantitative chest computed tomography (CT) scoring and NPPV failure and mortality in patients with COVID-19.DesignObservational study.SettingNonintensive care setting.ParticipantsA total of 112 patients consecutively admitted for COVID-19 pneumonia.InterventionsUsual care including various degrees of respiratory support.Measurements and Main ResultsThe semiquantitative CT score was calculated at hospital admission. Subgroups were identified according to the ventilation strategy used (oxygen delivered by Venturi mask n = 53; NPPV-responder n = 38; NPPV-failure n = 21). The study's primary endpoint was the use of NPPV. The secondary endpoints were NPPV failure and in-hospital death, respectively. CT score progressively increased among groups (six v nine v 14, p < 0.05 among all). CT score was an independent predictor of all study endpoints (primary endpoint: 1.25 [95% confidence interval {CI} 1.1-1.4], p = 0.001; NPPV failure: 1.41 [95% CI 1.18-1.69], p < 0.001; in-hospital mortality: 1.21 [95% CI 1.07-1.38], p = 0.003). According to receiver operator characteristics curve analysis, CT score was the most accurate variable for prediction of NPPV failure (area under the curve 0.862 with p < 0.001; p < 0.05 v other variables).ConclusionsThe authors reported the common and effective use of NPPV in patients with COVID-19 pneumonia. In the authors’ population, a semiquantitative chest CT analysis at hospital admission accurately identified those patients responding poorly to NPPV.

Evaluation of the long-term effect of polyhexamethylene guanidine phosphate in a rat lung model using conventional chest computed tomography with histopathologic analysis.

There have been no studies on the effects of polyhexamethylene guanidine phosphate (PHMG) after a long period of exposure in the rodent model. We aimed to evaluate long-term lung damage after PHMG exposure using conventional chest computed tomography (CT) and histopathologic analysis in a rat model. A PHMG solution was intratracheally administrated to 24 male rats. At 8, 26, and 52 weeks after PHMG instillation, conventional chest CT was performed in all rats and both lungs were extracted for histopathologic evaluation. At 52 weeks after PHMG instillation, four carcinomas had developed in three of the eight rats (37.5%). Bronchiolo-alveolar hyperplasia and adenoma were found in rats at 8, 26, and 52 weeks post-instillation. The number of bronchiolo-alveolar hyperplasia significantly increased over time (P-value for trend< 0.001). The severity of lung fibrosis and fibrosis scores significantly increased over time (P-values for trend = 0.002 and 0.023, respectively). Conventional chest CT analysis showed that bronchiectasis and linear density scores suggestive of fibrosis significantly increased over time (P-value for trend < 0.001). Our study revealed that one instillation of PHMG in a rat model resulted in lung carcinomas and progressive and irreversible fibrosis one year later based on conventional chest CT and histopathologic analysis. PHMG may be a lung carcinogen in the rat model.

Computed Tomography Analysis Based on Imaging Manifestations of Pulmonary Lymphoma

Objective: The imaging manifestations of lung lymphoma are complex and diverse. This paper discusses the pathological features of lung lymphoma and improves the clinical and radiologist’s understanding of the disease. Methods: Retrospective analysis of chest computed tomography (CT) image data of 58 patients with pulmonary lymphoma confirmed by clinical and histopathology. Results: CT diagnosis of 35 cases of pulmonary lymphoma, including 13 cases of mass nodular type, 15 cases of pneumonia consolidation type, 7 cases of mixed type, and the diagnostic accuracy rate was 60.34%. Airborne bronchial signs, normal vascular crossing signs, coexisting signs of multiple types of lesions, cross-lobe distribution signs, and ground glass signs are typical CT signs of pulmonary lymphoma. The average value of CT vital signs changes of the five types of lung lymphomas is 18%. The scan completion rate is 81%. The number of patients with five types of lung lymphoma were 17 cases of age &lt;35 years old, and 18 cases for age &gt;35 years old, with a balanced age distribution. Conclusions: CT examination is helpful for the diagnosis and differential diagnosis of pulmonary lymphoma.

The importance of central airway dilatation in patients with bronchiolitis obliterans.

BackgroundBronchiolitis obliterans (BO) is a clinical syndrome characterised by progressive small airway obstruction, causing significant morbidity and mortality. Central airway dilatation is one of its radiological characteristics, but little is known about the clinical and pathological associations between airway dilatation and BO.MethodsThis retrospective study consecutively included patients who underwent lung transplantation due to BO at Kyoto University Hospital from 2009 to 2019. Demographic and histopathological findings of the resected lungs were compared between patients with and without airway dilatation measured by chest computed tomography (CT) at registration for lung transplantation.ResultsOf a total of 38 included patients (median age, 30 years), 34 (89%) had a history of hematopoietic stem-cell transplantation, and 22 (58%) had airway dilatation based on CT. Patients with airway dilatation had a higher frequency of Pseudomonas aeruginosa isolation with greater residual volume than those without airway dilatation. Quantitative CT analysis revealed an increase in lung volume to predictive total lung capacity and a percentage of low attenuation volume <−950 HU at inspiration in association with the extent of airway dilatation. Airway dilatation on CT was associated with an increased number of bronchioles with concentric narrowing of the lumen and thickening of the subepithelium of the walls on histology.ConclusionsIn patients with BO, airway dilatation may reflect increased residual volume or air trapping and pathological extent of obstructive bronchioles, accompanied by a risk of Pseudomonas aeruginosa isolation. More attention should be paid to the development of airway dilatation in the management of BO.

A bi-stage feature selection approach for COVID-19 prediction using chest CT images.

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

PurposeTo evaluate the prognostic role of chest computed tomography (CT), alone or in combination with clinical and laboratory parameters, in COVID-19 patients during the first peak of the pandemic. MethodsA retrospective single-center study of 301 COVID-19 patients referred to our Emergency Department (ED) from February 25 to March 29, 2020. At presentation, patients underwent chest CT and clinical and laboratory examinations. Outcomes included discharge from the ED after improvement/recovery (positive outcome), or admission to the intensive care unit or death (poor prognosis). A visual quantitative analysis was formed using two scores: the Pulmonary Involvement (PI) score based on the extension of lung involvement, and the Pulmonary Consolidation (PC) score based on lung consolidation. The prognostic value of CT alone or integrated with other parameters was studied by logistic regression and ROC analysis. ResultsThe impact of the CT PI score [≥15 vs. ≤ 6] on predicting poor prognosis (OR 5.71 95 % CI 1.93−16.92, P = 0.002) was demonstrated; no significant association was found for the PC score. Chest CT had a prognostic role considering the PI score alone (AUC 0.722) and when evaluated with demographic characteristics, comorbidities, and laboratory data (AUC 0.841). We, therefore, developed a nomogram as an easy tool for immediate clinical application. ConclusionsVisual analysis of CT gives useful information to physicians for prognostic evaluations, even in conditions of COVID-19 emergency. The predictive value is increased by evaluating CT in combination with clinical and laboratory data.

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing.

Chest computed tomography and alveolar–arterial oxygen gradient as rapid tools to diagnose and triage mildly symptomatic COVID-19 pneumonia patients

BackgroundIn the coronavirus disease 2019 (COVID-19) pandemic, rapid clinical triage is crucial to determine which patients need hospitalisation. We hypothesised that chest computed tomography (CT) and alveolar-arterial oxygen tension ratio (A-a) gradient may be useful to triage these patients, since they reflect the severity of the pneumonia-associated ventilation/perfusion abnormalities.MethodsA retrospective analysis was performed in 235 consecutive patients suspected for COVID-19. The diagnostic protocol included low-dose chest CT and arterial blood gas analysis. In patients with CT-based COVID-19 pneumonia, the association between “need for hospitalisation” and A-a gradient was investigated by a multivariable logistic regression model. The A-a gradient was tested as a predictor for need for hospitalisation using receiver operating characteristic curve analysis and a logistic regression model.Results72 out of 235 patients (mean±sd age 55.5±14.6 years, 40% female) screened by chest CT showed evidence for COVID-19 pneumonia. In these patients, A-a gradient was shown to be a predictor of need for hospitalisation, with an optimal decision level (cut-off) of 36.4 mmHg (95% CI 0.70–0.91, p<0.001). The A-a gradient was shown to be independently associated with need for hospitalisation (OR 1.97 (95% CI 1.23–3.15), p=0.005; A-a gradient per 10 points) from CT severity score (OR 1.13 (95% CI 0.94–1.36), p=0.191), National Early Warning Score (OR 1.19 (95% CI 0.91–1.57), p=0.321) or peripheral oxygen saturation (OR 0.88 (95% CI 0.68–1.14), p=0.345).ConclusionLow-dose chest CT and the A-a gradient may serve as rapid and accurate tools to diagnose COVID-19 pneumonia and to select mildly symptomatic patients in need for hospitalisation.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.

BackgroundIt is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition.MethodsThis is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity.ResultsThe 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters.ConclusionDeep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Asthma with Irreversible Airway Obstruction in Smokers and Nonsmokers: Links between Airway Inflammation and Structural Changes

Background: The development of irreversible airway obstruction (IRAO) in asthma is related to lung/airway inflammatory and structural changes whose characteristics are likely influenced by exposure to tobacco smoke. Objective: To investigate the interplay between airway and lung structural changes, airway inflammation, and smoking exposure in asthmatics with IRAO. Methods: We studied asthmatics with IRAO who were further classified according to their smoking history, those with ≥20 pack-years of tobacco exposure (asthmatics with smoking-related IRAO [AwS-IRAO]) and those with <5 pack-years of tobacco exposure (asthmatics with nonsmoking-related IRAO [AwNS-IRAO]). In addition to recording baseline clinical and lung function features, all patients had a chest computed tomography (CT) from which airway wall thickness was measured and quantitative and qualitative assessment of emphysema was performed. The airway inflammatory profile was documented from differential inflammatory cell counts on induced sputum. Results: Ninety patients were recruited (57 AwS-IRAO and 33 AwNS-IRAO). There were no statistically significant differences in the extent of emphysema and gas trapping between groups on quantitative chest CT analysis, although Pi10, a marker of airway wall thickness, was significantly higher in AwS-IRAO (p = 0.0242). Visual analysis showed a higher prevalence of emphysema (p = 0.0001) and higher emphysema score (p < 0.0001) in AwS-IRAO compared to AwNS-IRAO and distribution of emphysema was different between groups. Correlations between radiological features and lung function were stronger in AwS-IRAO. In a subgroup analysis, we found a correlation between airway neutrophilia and emphysematous features in AwS-IRAO and between eosinophilia and both airway wall thickness and emphysematous changes in AwNS-IRAO. Conclusions: Although bronchial structural changes were relatively similar in smoking and nonsmoking patients with asthma and IRAO, emphysematous changes were more predominant in smokers. However, neutrophils in AwS-IRAO and eosinophils in AwNS-IRAO were associated with lung and airway structural changes.