Comparison of initial thin-section CT features in coronavirus disease 2019 pneumonia and other community-acquired pneumonia

Abstract Background Coronavirus disease 2019 (COVID-19) pneumonia caused similar symptoms to other community-acquired pneumonia (CAP). It is important to early quarantine suspected patients with COVID-19 pneumonia from patients with other CAP to reduce cross infection. The purpose of the study is to review and compare initial thin-section computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia and other community-acquired pneumonia (CAP). Methods 24 cases of COVID-19 pneumonia (14 males and 10 females; age range, 14-87 years; mean age, 48.0 years) and 28 cases of CAP caused by other pathogens (13 males and 15 females; age range, 24-85 years; mean age, 49.5 years) were included. Thin-section CT features of the lungs for all patients were retrospectively reviewed by two independent radiologists. Results There were no significant differences for the shape of main lesions, pure ground glass attenuation (GGA), mixed GGA with consolidation, air bronchogram, linear opacities, halo sign/reversed halo sign, cavitation and lymphadenopathy between the group of COVID-19 pneumonia and the group of other CAP. However, the frequency of crazy-paving appearance, vessel dilatation, bilaterally involvement and peripherally distribution were significantly higher in patients with COVID-19 compared with other CAP ( p =0.031, p =0.000, p =0.029 and p =0.009, respectively). Conversely, the frequencies of pure consolidation, tree-in-bud sign and pleural effusion were significantly higher in patients with CAP than in patients with COVID-19 pneumonia ( p =0.002, p =0.000 and p =0.048, respectively). Conclusion There are considerable overlaps in thin-section CT features between COVID-19 pneumonia and other CAP. However, the presence of crazy paving pattern, vessel dilation, bilateral involvement and peripheral distribution contributes to the diagnosis of COVID-19 pneumonia. While the presence of pure consolidation tree-in-bud sign, pleural effusion can be assisting in exclusive the diagnosis of COVID-19 pneumonia.

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Reversed halo sign on thin-section CT in a patient with non-specific interstitial pneumonia

British Journal of Radiology ◽

10.1259/bjr/43867123 ◽

2011 ◽

Vol 84 (1001) ◽

pp. e103-e105 ◽

Cited By ~ 4

Author(s):

S H Hong ◽

E-Y Kang ◽

B K Shin ◽

J J Shim

Keyword(s):

Interstitial Pneumonia ◽

Thin Section ◽

Halo Sign ◽

Reversed Halo Sign

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El Signo De Halo Invertido En Tomografía Computada De Tórax [The Inverted Halo Sign in Thoracic Computed Tomography]

Revista Médica de Panamá - ISSN 2412-642X ◽

10.37980/im.journal.rmdp.2020832 ◽

2020 ◽

Vol 40 (1) ◽

Author(s):

Rolando Reyna ◽

Karen Souza

Keyword(s):

Fungal Infections ◽

Ground Glass Opacity ◽

Pneumocystis Pneumonia ◽

Community Acquired Pneumonia ◽

Wegener Granulomatosis ◽

Lipoid Pneumonia ◽

Halo Sign ◽

Thoracic Computed Tomography ◽

Wide Range ◽

Reversed Halo Sign

ResumenEl signo del halo invertido se caracteriza por una opacidad central de vidrio esmerilado rodeado por una consolidación del espacio aéreo más densa en forma de una media luna o un anillo. El signo del halo invertido se ha informado en asociación con un amplia gama de enfermedades pulmonares, incluidas las infecciones fúngicas pulmonares invasivas, neumonía por pneumocystis, tuberculosis, neumonía adquirida en la comunidad, granulomatosis linfomatoide, granulomatosis de Wegener, neumonía lipoidea y sarcoidosis. También se observa en neoplasmas pulmonares e infarto y después de radioterapia y ablación por radiofrecuencia de neoplasias malignas pulmonares. También es conocido como signo de halo en reversa o signo del atolón.AbstractThe reversed halo sign is characterized by a central ground-glass opacity surrounded by denser air–space consolidation in the shape of a crescent or a ring. The reversed halo sign has been reported in association with a wide range of pulmonary diseases, including invasive pulmonary fungal infections, pneumocystis pneumonia, tuberculosis, community-acquired pneumonia, lymphomatoid granulomatosis,Wegener granulomatosis, lipoid pneumonia and sarcoidosis. It is also seen in pulmonary neoplasms and infarction, and following radiation therapy and radiofrequency ablation of pulmonary malignancies. It is also known as a reverse halo sign or atoll sign.

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Tomographic assessment of thoracic fungal diseases: a pattern and signs approach

Radiologia Brasileira ◽

10.1590/0100-3984.2017.0223 ◽

2018 ◽

Vol 51 (5) ◽

pp. 313-321 ◽

Cited By ~ 4

Author(s):

Pedro Paulo Teixeira e Silva Torres ◽

Marcelo Fouad Rabahi ◽

Maria Auxiliadora Carmo Moreira ◽

Pablo Rydz Pinheiro Santana ◽

Antônio Carlos Portugal Gomes ◽

...

Keyword(s):

Pleural Effusion ◽

Opportunistic Infections ◽

Fungal Infections ◽

Immunological Status ◽

Halo Sign ◽

Clinical Presentations ◽

Computed Tomography Scans ◽

Reversed Halo Sign ◽

Pulmonary Fungal Infections ◽

Considerable Morbidity

Abstract Pulmonary fungal infections, which can be opportunistic or endemic, lead to considerable morbidity and mortality. Such infections have multiple clinical presentations and imaging patterns, overlapping with those of various other diseases, complicating the diagnostic approach. Given the immensity of Brazil, knowledge of the epidemiological context of pulmonary fungal infections in the various regions of the country is paramount when considering their differential diagnoses. In addition, defining the patient immunological status will facilitate the identification of opportunistic infections, such as those occurring in patients with AIDS or febrile neutropenia. Histoplasmosis, coccidioidomycosis, and paracoccidioidomycosis usually affect immunocompetent patients, whereas aspergillosis, candidiasis, cryptococcosis, and pneumocystosis tend to affect those who are immunocompromised. Ground-glass opacities, nodules, consolidations, a miliary pattern, cavitary lesions, the halo sign/reversed halo sign, and bronchiectasis are typical imaging patterns in the lungs and will be described individually, as will less common lesions such as pleural effusion, mediastinal lesions, pleural effusion, and chest wall involvement. Interpreting such tomographic patterns/signs on computed tomography scans together with the patient immunological status and epidemiological context can facilitate the differential diagnosis by narrowing the options.

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Predicting COVID-19 Disease Progression with Chest CT Images

10.21203/rs.3.rs-80956/v1 ◽

2020 ◽

Author(s):

Hongqin Liang ◽

Xiaoming Qiu ◽

Liqiang Zhu ◽

Lihua Chen ◽

Xiaofei Hu ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Youden Index ◽

Unconditional Logistic Regression ◽

Linear Discriminant ◽

Halo Sign ◽

Reversed Halo Sign ◽

Sensitivity Specificity ◽

Ct Features ◽

Fisher’S Linear Discriminant

Abstract Background: Some mild patients can deteriorate to moderate or severe within a week with the natural progression of COVID-19.it has been crucial to early identify those mild cases and give timely treatment . The chest computed tomography (CT) has shown to be useful to assist clinical diagnosis of COVID-19.In this study, machine learning was used to develop an early-warning CT feature model for predicting mild patients with potential malignant progression.Methods：The total of 140 COVID-19 mild patients were collected. All patients at admission were divided into groups (alleviation group and exacerbation group) with or without malignant progression.The clinical and laboratory data at admission, the first CT, and the follow-up CT at critical stage of the two groups were compared with Chi-square test,.The CT features data (distribution, morphology,etc) were used to establish the prediction model by Fisher's linear discriminant method and Unconditional logistic regression algorithm. And the model was validated with 40 exception data.and the Area Under ROC curve (AUC) was used to evaluate the models.Results：The model filtered out three variables of CT features including distal air bronchogram, fibrosis,and reversed halo sign. Notably, the distal air bronchograms was less common in alleviation group, while the fibrosis and reversed halo sign were more common.The sensitivity, specificity and Youden index of unconditional logistic regression were 86.1%, 92.6% and 78.7%, For the analysis of Fisher's linear discriminant, the sensitivity, specificity and Youden index were 83.3%, 94.1% and 77.4%. The generalization ability of both models were consistent with sensitivity of 95.89%, specificity of 100%, and Youden index of 83.33%.Conclusions: The CT imaging features-based machine learning model has a high sensitivity for finding out the mild patients who are easy to deteriorate into severe/critical cases efficiently so that timely treatments came true for those patients,while largely help to relieve the medical pressure.

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