Chest Computed Tomography Analysis Research Articles

Quantitative analysis of chest CT in coronavirus infected patients and its correlation with clinical features

To explore the quantitative analysis results of different patterns of chest computed tomography (CT) in patients with coronavirus infection and its relationship with viral load and pathophysiological status. A retrospective clinical cohort study was conducted. Patients with coronavirus infection admitted to Qingdao Municipal Hospital from June 9 to 15, 2023 (all patients underwent chest CT examination within 24 hours after diagnosis) were enrolled. The patients were divided into coronavirus infection non-pneumonia group and coronavirus infection associated pneumonia group according to CT findings. Relevant baseline data, such as demographic characteristics, chest CT characteristics, and laboratory indicators within 12 hours before and after CT examination were collected from each group. Spearman correlation test was used to quantitatively analyze the correlation between CT features and laboratory indicators. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of each laboratory index for pneumonia in patients infected with coronavirus. Multiple linear regression analysis was used to explore the relationship between different CT patterns such as ground-glass opacity (GGO) and consolidation and ventilatory oxygenation status. A total of 171 patients were enrolled, including 44 patients in the coronavirus infection non-pneumonia group and 127 patients in the coronavirus infection associated pneumonia group (the incidence of pneumonia was 74.3%). Compared with patients with coronavirus infection alone, patients with coronavirus infection associated pneumonia had significantly lower lymphocyte count (LYM), oxygenation index (PaO2/FiO2), total lung capacity, GGO volume and GGO ratio, and significantly higher C-reactive protein (CRP), neutrophil/lymphocyte ratio (NLR), D-dimer, fraction of inspired oxygen (FiO2) level, real volume variation and consolidation ratio, the differences were all statistically significant. There were no statistically significant differences in the nucleocapin protein (N) gene cycle threshold (Ct) value and open reading frame (ORF) gene Ct value between the two groups. ROC curve analysis showed that, after adjusting for age, gender, CRP level and other related factors, compared with N gene Ct value, ORF gene Ct value, N gene Ct value+LYM, ORF gene Ct value+LYM, the LYM had the most potential diagnosis power for coronavirus infection associated pneumonia. The area under the ROC curve (AUC) of LYM for predicting coronavirus infection was 0.703. When the cut-off value of LYM was 0.7×109/L, the sensitivity was 55.5%, and the specificity was 79.5%, respectively. Multiple linear regression analysis showed that, when adjusted for consolidation ratio, age, gender, Hb and D-dimer levels, the GGO ratio in patients with coronavirus infection associated pneumonia was correlated with PaO2/FiO2 (β = -2.18, P < 0.001). When adjusted for GGO ratio, age, sex, Hb and D-dimer levels, the proportion of consolidation in patients with coronavirus infection associated pneumonia was correlated with PaCO2 (β = 0.36, P = 0.004). When adjusted for GGO ratio, the proportion of consolidation in patients with coronavirus infection associated pneumonia was also associated with NLR (β = 0.79, P = 0.006). LYM could be a potential marker for predicting coronavirus associated pneumonia, and the correlation seems to be independent of viral load. In addition, in the analysis of imaging features, GGO is associated with hypoxia, while consolidation is associated with PaCO2 level and inflammation. The increased proportion of consolidation in the whole lung may be detrimental to lung ventilation.

Effect of contrast media on CT bone density assessment: comparative analysis of low-dose chest CT and abdominopelvic CT

Detecting individuals with low bone mineral density (BMD) before clinical fractures occur may help improve the outcomes of osteoporosis and osteopenia. Although computed tomography (CT) is useful for opportunistic BMD measurement, the modality most suitable for opportunistic screening remains unclear. In this retrospective study, we compared the diagnostic performance of low-dose chest CT (LDCT) and contrast-enhanced abdominopelvic CT (APCT) for measuring BMD at L1 level using dual-energy X-ray absorptiometry (DEXA) as a reference in individuals who underwent LDCT, APCT, and DEXA assessments on the same day. We included 512 individuals (median age: 60 years; interquartile range, 55−65 years; 307 men). Both LDCT (r = 0.706; P < 0.001) and APCT (r = 0.643; P < 0.001) exhibited strong correlation with DEXA T-scores. As T-scores decreased, the relative difference between LDCT and APCT Hounsfield unit values increased (b = − 6.456; P < 0.001). LDCT outperformed APCT in diagnosing both osteoporosis (AUC, 0.865 vs. 0.833; P = 0.035) and low BMD (AUC, 0.844 vs. 0.815; P = 0.006), which may be attributable to the greater effect of intravenous contrast media on CT scan characteristics in individuals with lower T-scores. These results may help inform the selection of imaging methods suitable for screening.

Detecting new lung cancer cases using artificial intelligence: clinical and economic evaluation of a retrospective analysis of computed tomography scans 2 years after the COVID-19 pandemic

BACKGROUND: Chest computed tomography (CT) is the main modality used to diagnose lung lesions caused by COVID-19 infection. Since 2020, the use of this modality in the Krasnoyarsk krai has increased. However, the incidence of lung cancer decreased by 5.2%. The current situation has raised concerns about missing radiographic signs typical of lung cancer and has stimulated the search for new diagnostic modalities using artificial intelligence (AI) for data analysis. AIM: The aim of the study was to evaluate the feasibility of using an AI algorithm to search for lung nodules based on chest CT data obtained during the COVID-19 pandemic to identify lung cancer. MATERIALS AND METHODS: The retrospective study included chest CT scans of patients from Krasnoyarsk krai diagnosed with COVID-19 reported in the PACS base between 1 November 2020 and 28 February 2021. The interval between chest CT and AI analysis ranged from two years and one month to two years and five months. Chest-IRA algorithm was used. AI detected lung nodules with a volume greater than 100 mm3. The radiologists divided the results into three groups based on the potential for lung cancer. The assessment of the economic benefits of using the AI algorithm considered the cost of wages and savings in the treatment of early-stage lung cancer, which affects gross regional product. RESULTS: The AI algorithm identified nodules in 484 out of 10,500 CT scans. A total of 192 patients with a high potential for lung cancer, 103 with no signs and 60 with inconclusive signs were identified, and 112 patients with a high and moderate potential for lung cancer did not seek medical care. AI confirmed 100 (28.2%) histologically proven cases of lung cancer, with stages I–II detected in 35%. Using AI instead of radiologists would save 25 months and 4 days of work, which is equal to 2 million 430 thousand rubles. Expected budget savings due to early detection of lung cancer vary from 10 million 600 thousand to 12 million 500 thousand rubles for each 10,500 CTs. The total economic effect for a five-year period would be from 259 million 400 thousand rubles to 305 million 100 thousand rubles. CONCLUSIONS: The use of AI to evaluate chest CT scans demonstrates high performance in identifying lung nodules, including those in patients with COVID-19, confirming its potential use for early detection of incidental lung nodules that might otherwise be missed.

Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.

Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients. We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality. The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors. Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.

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.

Computed tomography findings and clinical manifestations in different clinical types of coronavirus disease 2019

OBJECTIVE: Since the coronavirus disease 2019 (COVID-19) outbreak in Wuhan in 2019, the virus has spread rapidly. We investigated the clinical and computed tomography (CT) characteristics of different clinical types of COVID-19. MATERIALS AND METHODS: We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China. All the patients were classified into the common (n = 65), severe (n = 18), or fatal (n = 6) type. CT features included lesion distribution, location, size, shape, edge, density, and the ratio of lung lesions to extra-pulmonary lesions. A COVID-19 chest CT analysis tool (uAI-discover-COVID-19) was used to calculate the number of infections from the chest CT images. RESULTS: Fatal type COVID-19 is more common in older men, with a median age of 65 years. Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients. Patients with fatal type COVID-19 were more likely to have underlying diseases. On CT examination, common type COVID-19 showed bilateral (68%), patchy (83%), ground-glass opacity (48%), or mixed (46%) lesions. Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions (56%). The infection ratio (IR) increased in the common type (2.4 [4.3]), severe type (15.7 [14.3]), and fatal type (36.9 [14.2]). The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients (P &lt; 0.05). However, in the fatal type group, only the IR in the right inferior lung (RIL) was statistically different from that in the right superior lung(RUL), right middle lung (RML), and the left superior lung (LSL) (P &lt; 0.05). CONCLUSION: The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different. Chest CT findings have unique characteristics in the different clinical types, which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.

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