Spectrum of atypical pulmonary manifestations of COVID-19 on computed tomography

BackgroundThe typical CT manifestations of COVID-19 pneumonia include ground-glass opacity (GGO) with or without consolidation and superimposed interlobular septal thickening. These are often rounded in morphology and frequently bilateral, multilobar, posterior, peripheral, and basilar in distribution. The various atypical CT features of COVID-19 are seldom described in the literature. The study aims to enumerate the atypical pulmonary CT features in patients with COVID-19 pneumonia in correlation with the disease severity.ResultsA total of 298 confirmed cases of COVID-19 pneumonia with positive reverse transcription polymerase chain reaction (RT-PCR) who underwent chest CT scans were retrospectively evaluated. The cohort included 234 (78.5%) men and 64 (21.5%) women and the mean age was 53.48 ± 15.74 years. The most common presenting symptoms were fever [n = 197 (66.1%)] and cough [n = 139 (46.6%)]. Out of 298 cases of COVID-19 pneumonia, 218 cases (73.1%) showed typical CT features while 63 cases (21.1%) showed atypical CT features with concurrent classical findings and the remaining 17 cases (5.8%) were normal. Among the atypical CT features, the most common was pulmonary cysts [n = 27 (9%)]. The other features in the order of frequency included pleural effusion [n = 17 (5.7%)], nodules [n = 13 (4.3%)], bull's eye/target sign[n = 4 (1.3%)], cavitation [n = 3 (1.0%)], spontaneous pneumothorax [n = 2 (0.6%)], hilar lymphadenopathy [n = 2 (0.6%)], spontaneous pneumo-mediastinum with subcutaneous emphysema [n = 1 (0.3%)], Halo sign [n = 1 (0.3%)], empyema [n = 1 (0.3%)] and necrotizing pneumonia with abscess [n = 1 (0.3%)].ConclusionCT imaging features of COVID-19 pneumonia while in a vast majority of cases is classical, atypical diverse patterns are also encountered. A comprehensive knowledge of various atypical presentations on imaging plays an important role in the early diagnosis and management of COVID-19.

Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features.

Background: Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis. Methods: Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated. Results: Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999). Conclusion: COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.

Frequent clinical and radiological manifestations of the Novel SARS-CoV-2: A review article.

Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the novel "severe acute respiratory syndrome coronavirus-2" (SARS-CoV-2) and is rapidly spreading worldwide. This review is designed to highlight the most common clinical features and computed tomography (CT) signs of patients with COVID-19 and to elaborate the most significant signs indicative of COVID-19 diagnosis. This review involved five original articles with both clinical and radiological features of COVID-19 published during Jan and Mar 2020. In this review, the most frequent symptoms of COVID-19 were fever and cough. Myalgia, fatigue, sore throat, headache, diarrhea, and dyspnea were less common manifestations. Nausea and vomiting were rare. Ground-glass opacity (GGO) was the most common radiological finding on CT, and mixed GGO with consolidation was reported in some cases. In addition, elevated C-reactive protein and lymphopenia are the pertinent laboratory findings of COVID-19. CT is an effective and important imaging tool for both diagnosis and follow-up COVID-19 patients with varied features, duration, and course of the disease. Bilateral GGOs, especially in the periphery of the lungs with or without consolidation, are the hallmark of COVID-19.

Typical chest CT features can determine the severity of COVID-19: A systematic review and meta-analysis of the observational studies.

BACKGROUND: It remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19. METHODS: PubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger's test. RESULTS: A total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81-74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by crazy paving (OR 7.60, 95% CI 3.82-15.14), linear opacity (OR 3.27, 95% CI 1.10-9.70), and GGO (OR 1.37, 95% CI 1.08-1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85-6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74-6.79). CONCLUSIONS: Our meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.

Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19.

OBJECTIVE: To investigate in the CT manifestations of severe and critical Coronavirus Disease 2019 (COVID-19) patients. METHODS: Medical data was collected for 2 severe patients and 4 critical COVID-19 patients from onset to their recovery. Three or four CT scans for each patient were taken. The semi-quantitative analysis method was introduced for lesion and its distribution area. RESULTS: The ground-glass opacities (GGO) and mixed GGO with consolidation were found as the most frequent features. Consolidation followed, and the appearance of stripes which showed an increasing trend before the patient was discharged. Consolidation was associated with clinical severity and disease progression, and the rapid change of the lesion in a short period of time was also a notable feature within 2-3 weeks. After being discharged, the efficacy of treatment could be demonstrated by a follow up CT scan. The distribution of lesion also showed dynamic progress in the follow up CT scan. CONCLUSION: CT scans in the whole course provided the entire inflammation information to assess clinical severity, disease progression and the treatment efficacy for COVID-19.

CT features of coronavirus disease 2019 (COVID-19) in the original district of this disease (Wuhan): a pictorial review.

On December 31, 2019, the Wuhan Health Commission reported the discovery of an "unexplained" pneumonia for the first time; the pathogen was confirmed as novel coronavirus pneumonia (2019-nCoV) on January 7, 2020. As one of the important examination methods for the Corona Virus Disease 2019 (COVID-19), Computed Tomography (CT) examination plays an important role in the clinical discovery of suspected cases, diagnosis, and treatment review. This paper reviews the published papers in order to offer help in early clinical screening, disease diagnosis, disease severity determination and post-treatment review.

[Comparison of Recovery Phase CT Features between Mild/moderate and Severe/critical Coronavirus Disease 2019 Patients].

Objective To investigate the computed tomographc(CT)features of mild/moderate and severe/critical cases of coronavirus disease 2019(COVID-19)in the recovery phase. Methods Totally 63 discharged patients in Wuhan,China,who underwent both chest CT and reverse transcription-polymerase chain reaction(RT-PCR)from February 1 to February 29,2020,were included.With RT-PCR as a gold standard,the performance of chest CT in diagnosing COVID-19 was assessed.Patients were divided into mild/moderate and severe/critical groups according to the disease conditions,and clinical features such as sex,age,symptoms,hospital stay,comorbidities,and oxygen therapy were collected.CT images in the recovery phase were reviewed in terms of time from onset,CT features,location of lesions,lobe score,and total CT score. Results There were 37 patients in the mild/moderate group and 26 in the severe/critical group. Compared with the mild/moderate patients,the severe/critical patients had older age [(43±16) years vs. (52±16) years; t=2.10, P=0.040], longer hospital stay [(15±6)d vs. (19±7)d; t=2.70, P=0.009], higher dyspnea ratio (5.41% vs. 53.85%; χ2=18.90, P<0.001), lower nasal oxygen therapy ratio (81.08% vs. 19.23%;χ2=23.66, P<0.001), and higher bi-level positive airway pressure ventilation ratio (0 vs. 57.69%; χ2=25.62, P<0.001). Time from onset was (23±6) days in severe/critical group, significantly longer than that in mild/moderate group [(18±7) days] (t=3.40, P<0.001). Severe/critical patients had significantly higher crazy-paving pattern ratio (46.15% vs.10.81%;χ2=4.24, P=0.039) and lower ground-glass opacities ratio (15.38% vs. 67.57%; χ2=16.74, P<0.001) than the mild/moderate patients. The proportion of lesions in peripheral lung was significantly higher in mild/moderate group than in severe/critical group (78.38% vs.34.61%; χ2=13.43, P<0.001), and the proportion of diffusely distributed lesions was significantly higher in severe/critical group than in mild/moderate group (65.38% vs.10.81%; χ2=20.47, P<0.001). Total CT score in severe/critical group was also significantly higher in severe/critical group than in mild/moderate group [11 (8,17) points vs. 7 (4,9) points; Z=3.81, P<0.001]. Conclusions The CT features in the recovery stage differ between mild/moderate and severe/critical COVID-19 patients.The lung infiltration is remarkably more severe in the latter.

Chest computed tomography findings and dynamic changes of severe coronavirus disease 2019 / 中华传染病杂志

Objective@#To investigate the features of chest CT imaging and dynamic changes of severe coronavirus disease 2019 (COVID-19).@*Methods@#The clinical and computed tomography (CT) data of 17 patients diagnosed with severe COVID-19 admitted to Chongqing Public Health Medical Center from January 24 to February 6, 2020 were collected. The first chest CT manifestations and the dynamic changes of imaging during treatment were retrospectively analyzed.@*Results@#The first chest CT manifestations of the 17 patients showed that 16 cases presented with peripheral and subpleural distributions, and 2 cases presented with 3 lobes involved, one case with 4 lobes involved and 14 cases with 5 lobes involved, and 17 cases presented with ground-glass opacities, ten cases with consolidation, seven cases with subpleural line, nine cases with air bronchogram, 3 cases with thickened lobular septum, two cases with bronchiectasis, two cases with pleural effusion, two cases with lymphadenopathy with the short diameter of 1.0-1.2cm. Among 16 patients who underwent repeated CT examination, the lesions of 8 patients showed continuous improvement, and those of the other 8 patients showed fluctuating changes.@*Conclusions@#The CT findings of severe COVID-19 patients are mainly ground-glass opacities and consolidation, with the peripheral distribution. The range of lesions is wide, with 5-lobe involvement mostly. Lymphadenopathy or pleural effusion is rare. Chest CT is useful for the evaluation for the therapeutic effects.