Efficacy of different doses of corticosteroids in treating severe COVID-19 pneumonia.

BACKGROUND: To investigate the efficacy of different doses of corticosteroids in treating severe coronavirus disease 2019 (COVID-19) pneumonia. METHODS: Between May 01, 2023, and June 20, 2023, 48 patients with severe COVID-19 pneumonia were treated at the Department of Respiratory and Critical Care Medicine of Jinan Fourth People's Hospital. The observation group (21 patients) received standard care and high-dose corticosteroids, (high-dose group). The control group (27 patients) received standard care and low-dose corticosteroids (low-dose group). We collected baseline data and recorded inflammatory marker levels after 3 days of treatment, body temperature recovery time, length of stay, and 28-day all-cause mortality. The results of outpatient follow-up were recorded after 1 month. RESULTS: There were no significant differences in 28-day mortality and length of stay. The number of days it took for body temperature to return to normal in the high-dose group was less than in the low-dose group. The high-dose group had significantly more reduced inflammatory factors (C-reactive protein (CRP), interleukin-6 (IL-6). A total of 20 discharged patients were given 8-16 mg of methylprednisolone, depending on chest computed tomography (CT) and clinical symptoms after 1 month; in all discharged patients using oral corticosteroids, CT features improved. CONCLUSION: High-dose corticosteroids had a significantly positive effect on the reduction of inflammatory factors and shortening body temperature recovery time. In the treatment of severe COVID-19 pneumonia, early administration of high-dose, short-course corticosteroids should be implemented.

Image quality of lung perfusion with photon-counting-detector CT: comparison with dual-source, dual-energy CT.

PURPOSE: To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). METHODS: Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. RESULTS: Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%). CONCLUSION: PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. CLINICAL RELEVANCE STATEMENT: The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. KEY POINTS: The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events.

BACKGROUND. Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asian country to initiate a national LDCT lung cancer screening program, although CAC-related outcomes are poorly explored. OBJECTIVE. The purpose of this article is to evaluate CAC prevalence and severity using visual analysis and artificial intelligence (AI) methods and to characterize CAC's association with major adverse cardiovascular events (MACEs) in patients undergoing LDCT in Korea's national lung cancer screening program. METHODS. This retrospective study included 1002 patients (mean age, 62.4 ± 5.4 [SD] years; 994 men, eight women) who underwent LDCT at two Korean medical centers between April 2017 and May 2023 as part of Korea's national lung cancer screening program. Two radiologists independently assessed CAC presence and severity using visual analysis, consulting a third radiologist to resolve differences. Two AI software applications were also used to assess CAC presence and severity. MACE occurrences were identified by EMR review. RESULTS. Interreader agreement for CAC presence and severity, expressed as kappa, was 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 60.1% by AI software I, and 56.6% by AI software II. CAC severity was mild, moderate, and severe by consensus visual analysis in 28.0%, 10.3%, and 15.1%; by AI software I in 39.9%, 14.0%, and 6.2%; and by AI software II in 34.9%, 14.3%, and 7.3%. MACEs occurred in 36 of 625 (5.6%) patients with follow-up after LDCT (median, 1108 days). MACE incidence in patients with no, mild, moderate, and severe CAC for consensus visual analysis was 1.1%, 5.0%, 2.9%, and 8.6%, respectively (p < .001); for AI software I, it was 1.3%, 3.0%, 7.9%, and 11.3% (p < .001); and for AI software II, it was 1.2%, 3.4%, 7.7%, and 9.6% (p < .001). CONCLUSION. For Korea's national lung cancer screening program, MACE occurrence increased significantly with increasing CAC severity, whether assessed by visual analysis or AI software. The study is limited by the large sex imbalance for Korea's national lung cancer screening program. CLINICAL IMPACT. The findings provide reference data for health care practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.

Imaging patterns of Lophomonas blattarum infection in the respiratory tract: a registry-based analysis.

BACKGROUND: Lophomonas blattarum is an emerging protozoan that mostly infects the lower respiratory tract and causes pulmonary lophomoniasis. Radiologic findings in patients with pulmonary lophomoniasis have yet to be studied. Thus, we conducted a registry-based clinical investigation to evaluate the radiologic findings of lophomoniasis. METHODS: In this cross-sectional study, 34 Lophomonas positive patients were enrolled. Demographic data, relevant characteristics, and radiologic findings of the patients were recorded and analyzed. RESULTS: Thirty-four (male = 18, female = 16) patients with an average age of 52.21 ± 20.48 years old were examined. Radiological findings such as Alveolar consolidation (26.5%), Ground glass opacity (5.9%), Centrilobular nodules (23.5%), Tree -in- bud (38.2%), Cavitation (23.5%), Pleural effusion (23.5%), Interstitial opacity (8.8%), Lymphadenopathy (23.5%), Bronchocele (5.9%), Bronchiectasis (29.4%), Nodules (8.8%) and Mass (11.8%) were obtained, that the frequency of all radiological findings was less than 50%. CONCLUSION: In this study, the most common radiological findings in patients with lophomoniasis were tree-in-bud nodules, alveolar consolidation, bronchiectasis, and centrilobular nodules which were mostly seen in the right lung and its middle and lower lobes. Given that the radiologic findings of this disease are unknown, it can be considered in differential diagnosis.

Dose-response relationship between lung function and chest imaging response to silica exposures in artificial stone manufacturing workers.

BACKGROUND: Occupational exposure to artificial stone, a popular material used for countertops, can cause accelerated silicosis, but the precise relationship between silica dose and disease development is unclear. OBJECTIVES: This study evaluated the impact of silica exposure on lung function and chest imaging in artificial stone manufacturing workers. METHODS: Questionnaire and spirometry assessments were administered to workers in two plants. A high-exposure subset underwent further evaluation, including chest CT and DLco. Weighting factors, assigned as proxies for silica exposure, were based on work tasks. Individual cumulative exposures were estimated using area concentration measurements and time spent in specific areas. Exposure-response associations were analyzed using linear and logistic regression models. RESULTS: Among 65 participants, the mean cumulative silica exposure was 3.61 mg/m3-year (range 0.0001 to 44.4). Each 1 mg/m3-year increase was associated with a 0.46% reduction in FVC, a 0.45% reduction in FEV1, and increased lung function abnormality risk (aOR = 1.27, 95% CI = 1.03-1.56). Weighting factors correlated with cumulative exposures (Spearman correlation = 0.59, p < 0.0001), and weighted tenure was associated with lung function abnormalities (aOR = 1.04, 95% CI = 1.01-1.09). Of 37 high-exposure workers, 19 underwent chest CT, with 12 (63%) showing abnormal opacities. Combining respiratory symptoms, lung function, and chest X-ray achieved 91.7% sensitivity and 75% specificity for predicting chest CT abnormalities. CONCLUSION: Lung function and chest CT abnormalities occur commonly in artificial stone workers. For high-exposure individuals, abnormalities on health screening could prompt further chest CT examination to facilitate early silicosis detection.

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19.

INTRODUCTION: In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results. METHODS: We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters. RESULTS: Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively. CONCLUSIONS: These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis.

BACKGROUND: There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules. METHODS: An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used. RESULTS: Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively. CONCLUSION: DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.

Hospital admission rates and related outcomes among adult Aboriginal australians with bronchiectasis - a ten-year retrospective cohort study.

BACKGROUND: This study assessed hospitalisation frequency and related clinical outcomes among adult Aboriginal Australians with bronchiectasis over a ten-year study period. METHOD: This retrospective study included patients aged ≥ 18 years diagnosed with bronchiectasis between 2011 and 2020 in the Top End, Northern Territory of Australia. Hospital admissions restricted to respiratory conditions (International Classification of Diseases (ICD) code J) and relevant clinical parameters were assessed and compared between those with and without hospital admissions. RESULTS: Of the 459 patients diagnosed to have bronchiectasis, 398 (87%) recorded at least one respiratory related (ICD-J code) hospitalisation during the 10-year window. In comparison to patients with a recorded hospitalisation against those without-hospitalised patients were older (median 57 vs 53 years), predominantly females (54 vs 46%), had lower body mass index (23 vs 26 kg/m2) and had greater concurrent presence of chronic obstructive pulmonary disease (COPD) (88 vs 47%), including demonstrating lower spirometry values (forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) (median FVC 49 vs 63% & FEV1 36 vs 55% respectively)). The total hospitalisations accounted for 3,123 admissions (median 4 per patient (IQR 2, 10)), at a median rate of 1 /year (IQR 0.5, 2.2) with a median length of 3 days (IQR 1, 6). Bronchiectasis along with COPD with lower respiratory tract infection (ICD code-J44) was the most common primary diagnosis code, accounting for 56% of presentations and 46% of days in hospital, which was also higher for patients using inhaled corticosteroids (81 vs 52%, p = 0.007). A total of 114 (29%) patients were recorded to have had an ICU admission, with a higher rate, including longer hospital stay among those patients with bronchiectasis and respiratory failure related presentations (32/35, 91%). In multivariate regression model, concurrent presence of COPD or asthma alongside bronchiectasis was associated with shorter times between subsequent hospitalisations (-423 days, p = 0.007 & -119 days, p = 0.02 respectively). CONCLUSION: Hospitalisation rates among adult Aboriginal Australians with bronchiectasis are high. Future interventions are required to explore avenues to reduce the overall morbidity associated with bronchiectasis among Aboriginal Australians.

Effectiveness of opportunistic osteoporosis screening on chest CT using the DCNN model.

OBJECTIVE: To develop and evaluate a deep learning model based on chest CT that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images, and explore the feasibility and effectiveness of the model based on the lumbar 1 vertebral body alone. MATERIALS AND METHODS: The chest CT images of 1048 health check subjects from January 2021 to June were retrospectively collected as the internal dataset (the segmentation model: 548 for training, 100 for tuning and 400 for test. The classification model: 530 for training, 100 for validation and 418 for test set). The subjects were divided into three categories according to the quantitative CT measurements, namely, normal, osteopenia and osteoporosis. First, a deep learning-based segmentation model was constructed, and the dice similarity coefficient(DSC) was used to compare the consistency between the model and manual labelling. Then, two classification models were established, namely, (i) model 1 (fusion feature construction of lumbar vertebral bodies 1 and 2) and (ii) model 2 (feature construction of lumbar 1 alone). Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of the models, and the Delong test was used to compare the areas under the curve. RESULTS: When the number of images in the training set was 300, the DSC value was 0.951 ± 0.030 in the test set. The results showed that the model 1 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.990, 0.952 and 0.980; the model 2 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.983, 0.940 and 0.978. The Delong test showed that there was no significant difference in area under the curve (AUC) values between the osteopenia group and osteoporosis group (P = 0.210, 0.546), while the AUC value of normal model 2 was higher than that of model 1 (0.990 vs. 0.983, P = 0.033). CONCLUSION: This study proposed a chest CT deep learning model that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images. We further constructed the comparable model based on the lumbar 1 vertebra alone which can shorten the scan length, reduce the radiation dose received by patients, and reduce the training cost of technologists.

Computed tomographic findings in TBX4 mutation: a common cause of severe pulmonary artery hypertension in children.

BACKGROUND: Mutations in the T-Box 4 (TBX4) gene are a lesser-known cause of heritable pulmonary arterial hypertension (PAH). Patients with heritable PAH typically have worse outcomes when compared with patients with idiopathic PAH, yet little is known about the phenotypical presentation of this mutation. OBJECTIVE: This article reviews the pattern of chest CT findings in pediatric patients with PAH and TBX4 mutations and compares their radiographic presentation with those of age-matched patients with PAH but without TBX4 mutations. MATERIALS AND METHODS: A retrospective chart review of the pulmonary arterial hypertension database was performed. Pediatric patients with PAH-confirmed TBX4 mutations and an available high CT were included. Fifteen (9 females) patients met the inclusion criteria. Fourteen (8 females) age-matched controls with diagnosed PAH but without TBX4 mutations were also evaluated. The median age at diagnosis was 7.4 years (range: 0.1-16.4 years). Demographic information and clinical outcomes were collected. CTs of the chest were reviewed for multiple airway, parenchymal, and structural abnormalities (16 imaging findings in total). Chi-square tests were used to compare the prevalence of each imaging finding in the TBX4 cohort compared to the control group. RESULTS: Patients with TBX-4 mutations had increased presence of peripheral or subpleural irregularity (73% vs 0%, P < 0.01), cystic lucencies (67% vs 7%, P < 0.01), and linear or reticular opacity (53% vs 0%, P < 0.01) compared to the control group. Ground glass opacities, bronchiectasis, and centrilobular nodules were not significantly different between the two patient groups (P > 0.05). CONCLUSION: TBX4 mutations have distinct imaging phenotypes in pediatric patients with PAH. Compared to patients without this mutation, patients with TBX-4 genes typically present with peripheral or subpleural irregularity, cystic lucencies, and linear or reticular opacity.

Classification of benign and malignant pulmonary nodule based on local-global hybrid network.

BACKGROUND: The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE: In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS: First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS: Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. CONCLUSION: The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.

The feasibility of using acoustic measures for predicting the Total Opacity Scores of chest computed tomography scans in patients with COVID-19.

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case-control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI.

Phenotype and Clinicoradiological Differences in Multifocal and Focal Bronchiectasis.

Introduction: Bronchiectasis is a chronic progressive respiratory disease characterized by permanent dilatation of the bronchi. It is a complex condition with numerous different etiologies, co-morbidities, and a heterogeneous presentation. As we know, there is a lack of studies that describe the differences and compare the characteristics between focal and multifocal bronchiectasis. The aim of this study is to identify differences in clinical characteristics presentation, severity or distribution in focal and multifocal bronchiectasis, and prognostic implications. Methods: 126 patients with computed tomography (CT)-verified bronchiectasis were enrolled. Baseline characteristics that included age, sex, smoking history, and respiratory symptoms were recorded, with special attention paid to hemoptysis appearance, body mass index, and comorbidities. The type of bronchiectasis determined by CT scan and the modified Reiff scores indicating radiological severity were recorded. Patients were divided in two groups (I is focal and II is multifocal). Results: There were no statistically significant differences in age, smoking status, comorbidity, and BMI between the two groups. Multifocality was associated with a significantly higher proportion of females (p = 0.014), the rate of hemoptysis (p = 0.023), and the number of hospitalizations, but not of exacerbations and prevalence of immunodeficiency (p = 0.049). Significantly, a high number of subjects with multifocality had bronchiectasis of moderate severity, and post-infective and asthma-associated phenotypes were the dominant in this group. Unexpectedly, the cystic and varicose radiological phenotype (which need more time to develop) were more dominant in the focal group. The cylindrical phenotype was equally observed in the multifocal and focal groups. Conclusions: Our study suggests that multifocality is not related to age, number of exacerbations, or radiological phenotype, but it seems to be associated with the clinical post-infective phenotype, immunodeficiency, frequent hospitalizations, and severity. Thus, the presence of multifocal bronchiectasis may act as a biomarker of severity and poor outcomes in these patients.

[Study on evaluation method of circular small shadow profusion in chest CT reconstruction images of pneumoconiosis].

Objective: To select chest CT image patterns for the diagnosis of pneumoconiosis and establish a method for determining the profusion of circular small shadows in chest CT. Methods: In April 2021, 66 cases of occupational pneumoconiosis patients with digital radiography (DR) chest radiographs and chest CT imaging data with circular small shadow as the main manifestations were selected as the study objects. 1.5 mm and 5 mm chest CT axial images, 1 mm and 5 mm chest CT coronal multi-plane recombination (MPR) images, and 5 mm chest CT coronal maximum intensity projection (MIP) images were used to observe the different characteristics of pneumoconiosis patients, and were compared and analyzed with DR chest radiographs to establish the experimental chest CT standards. The consistency of the profusion results between the experimental chest CT standards and GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis was verified. Results: All the 66 objects were male, including 33 cases of stage Ⅰ pneumoconiosis, 17 cases of stage Ⅱ pneumoconiosis and 16 cases of stage Ⅲ pneumoconiosis. By observing five chest CT images of 66 objects, we found that chest CT images of different modes could clearly display and identify abnormal images such as small circular shadow, large shadow, small shadow aggregation, honeycomb glass shadow, flake glass shadow, uniform low-profusion glass shadow, mesh glass shadow, cable shadow, linear shadow, subpleural spinous shadow, subpleural nodules, various kinds of emphysema and lung texture distortion and fracture. Small shadow aggregation was usually accompanied by the appearance of large shadow. The vascular shadows in 5 mm CT images had good ductility, and small nodules were easy to distinguish. The coronal MIP image of 5 mm chest CT used edge enhancement technology, which was prone to small shadow fusion and fibrotic shadow fusion. The coronal MPR image of 5 mm chest CT was highly consistent with the DR chest radiographs in terms of the integrity of film reading. GBZ 70-2015 standard was used to compare the profusion of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, and the consistency test Kappa=0.64. GBZ 70-2015 standard and experimental chest CT standard were used to compare the profusion results of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, respectively, and the consistency test Kappa=0.80, with high consistency. Conclusion: 5 mm coronal MPR image is suitable for chest CT imaging in the diagnosis of pneumoconiosis. Following the selection path and method of GBZ 70-2015 profusion criterion, the established experimental chest CT standard in determining the profusion of small circular shadows in 5 mm coronal MPR images of chest CT with pneumoconiosis has a high consistency with GBZ 70-2015 standard.

Common and Uncommon CT Findings in CVID-Related GL-ILD: Correlations with Clinical Parameters, Therapeutic Decisions and Potential Implications in the Differential Diagnosis.

PURPOSE: To investigate computed tomography (CT) findings of Granulomatous Lymphocytic Interstitial Lung Disease (GL-ILD) in Common Variable Immunodeficiency (CVID), also in comparison with non-GL-ILD abnormalities, correlating GL-ILD features with functional/immunological parameters and looking for GL-ILD therapy predictive elements. METHODS: CT features of 38 GL-ILD and 38 matched non-GL-ILD subjects were retrospectively described. Correlations of GL-ILD features with functional/immunological features were assessed. A logistic regression was performed to find a predictive model of GL-ILD therapeutic decisions. RESULTS: Most common GL-ILD CT findings were bronchiectasis, non-perilymphatic nodules, consolidations, Ground Glass Opacities (GGO), bands and enlarged lymphnodes. GL-ILD was usually predominant in lower fields. Multiple small nodules (≤10 mm), consolidations, reticulations and fibrotic ILD are more indicative of GL-ILD. Bronchiectasis, GGO, Reticulations and fibrotic ILD correlated with decreased lung performance. Bronchiectasis, GGO and fibrotic ILD were associated with low IgA levels, whereas high CD4+ T cells percentage was related to GGO. Twenty out of 38 patients underwent GL-ILD therapy. A model combining Marginal Zone (MZ) B cells percentage, IgA levels, lower field consolidations and lymphnodes enlargement showed a good discriminatory capacity with regards to GL-ILD treatment. CONCLUSIONS: GL-ILD is a lower field predominant disease, commonly characterized by bronchiectasis, non-perilymphatic small nodules, consolidations, GGO and bands. Multiple small nodules, consolidations, reticulations and fibrotic ILD may suggest the presence of GL-ILD in CVID. MZ B cells percentage, IgA levels at diagnosis, lower field consolidations and mediastinal lymphnodes enlargement may predict the need of a specific GL-ILD therapy.

Chest CT Lung Abnormalities 1 Year after COVID-19: A Systematic Review and Meta-Analysis.

Background Radiological lung sequelae may explain the persistence of respiratory complaints in post-COVID-19 condition (long-COVID). Purpose To perform a systematic review and meta-analysis of the prevalence and type of COVID-19 residual lung abnormalities at 1-year chest CT. Materials and Methods A literature search of PubMed, Web of Science, Embase, and Medline databases was performed from January 2020 to January 2023. Full-text reports of CT lung sequelae in adults (≥18 years) with confirmed COVID-19 at 1-year follow-up were included. The prevalence of any residual lung abnormality and type (fibrotic or not) was analyzed according to the Fleischner Glossary. The meta-analysis included studies with chest CT data assessable in no less than 80% of individuals. A random-effects model was used to estimate pooled prevalence. Multiple sub-group (country, journal category, methodological quality, study setting, outcomes) and meta-regression analyses were performed to identify potential sources of heterogeneity. I2 statistics estimated low (25%), moderate (26-50%) and high (>50%) heterogeneity. 95% Prediction Intervals (95% PIs) were computed to describe the expected estimates range. Results Of 22 709 records, 21 studies were reviewed (20 prospective, 9 from China, and 7 in radiology journals). The meta-analysis included 14 studies with chest CT data in 1854 of 2043 individuals (M/F: 1109/934). Estimates of lung sequelae were highly heterogeneous (7.1-96.7%), with a pooled frequency of 43.5% (I2=94%; 95% PI: 5.9%, 90.4%). This also applied to single non-fibrotic changes, including ground glass opacity, consolidations, nodules/masses, parenchymal bands, and reticulations. The prevalence range of fibrotic traction bronchiectasis/bronchiolectasis was 1.6-25.7% (I2=93%; 95% PI: 0.0%, 98.6%;); honeycombing was unremarkable (0-1.1%; I2=58%; 95% PI: 0%, 60%). Lung sequelae were unrelated to any characteristics of interest. Conclusion The prevalence of COVID-19 lung sequelae at 1-year chest CT is highly heterogeneous among studies. Heterogeneity determinants remain unknown suggesting caution in data interpretation with no convincing evidence. PROSPERO (CRD42022341258) Keywords: COVID-19 pneumonia, pulmonary fibrosis, chest CT, long-COVID, systematic review, metaanalysis See also the editorial by Parraga and Svenningsen in this issue.

Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia.

To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software (3D Slicer) to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.

The chest CT signs for pulmonary veno-occlusive disease correlate with pulmonary haemodynamics in systemic sclerosis.

OBJECTIVE: Pulmonary arterial hypertension associated with systemic sclerosis (PAH-SSc) sometimes accompanies pulmonary veno-occlusive disease (PVOD). We aimed to reveal the relation between clinical signs of PVOD and severing of pulmonary vasculopathy in SSc. METHODS: This study comprised 52 consecutive SSc patients who had pulmonary haemodynamic abnormalities (mPAP > 20 mmHg, PVR > 2 W.U. or PAWP > 15 mmHg). The chest CT scan was evaluated in all patients. Patients were divided into two groups, the 0-1 group and the 2-3 group, according to the number of chest CT signs for PVOD, including 1) mediastinal lymph node enlargement, 2) thickened interlobular septal wall, and 3) ground glass opacity. Pulmonary haemodynamics, echocardiography and MRI-based cardiac function, pulmonary function, and serum biomarkers were compared between the two groups. RESULTS: Mediastinal lymph node enlargement, thickened interlobular septal wall, and ground glass opacity were observed in 11 (21%), 32 (62%), and 11 (21%) patients, respectively. The 2-3 group (n = 15) had higher mPAP (p= 0.02) while lower DLco/VA (p= 0.02) compared with the 0-1 group (n = 37). Other parameters, including PAWP, cardiac output, left ventricular ejection fraction, left atrial diameter, forced vital capacity, brain natriuretic peptide, and Krebs von den Lunge-6 were not different between the two groups. CONCLUSION: The CT signs for PVOD had positive correlation with mPAP but negative correlation with DLco in SSc patients, indicating that PAH-SSc may reflect a spectrum of pulmonary vascular disease that ranges from the pulmonary artery to the vein.

Respiratory microbiota and radiomics features in the stable COPD patients.

BACKGROUNDS: The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them. METHODS: Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below - 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/[Formula: see text] and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed. RESULTS: Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/[Formula: see text] than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA. CONCLUSIONS: Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers.

COVID-19 lesion detection and segmentation-A deep learning method.

PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. MATERIALS AND METHODS: Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. RESULTS: Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. CONCLUSION: The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction.