Predictive value of chest CT scoring in COVID-19 patients in Wuhan, China: A retrospective cohort study

BACKGROUND

Computed tomography (CT) findings of COVID-19 patients were demonstrated by cases series and descriptive studies, but quantitative analysis performed by clinical doctors and studies on its predictive value were rarely seen. The aim of the study is to analyze CT score in COVID-19 patients and explore its predictive value.

MATERIALS AND METHODS

We conducted a retrospective cohort study among confirmed COVID -19 patients with available CT images between February 8, 2020 and March 7, 2020. The lung was divided into six zones by the level of tracheal carina and the level of inferior pulmonary vein bilaterally on CT. Ground-glass opacity (GGO), consolidation, crazy-paving pattern and overall lung involvement were rated by Likert scale of 0-4 or binary as 0 or 1. Global severity score for each targeted pattern was calculated as total score of six zones.

RESULTS

There were 53 patients and 137 CT scans included in the study. There were 18(34%) of the patients classified as moderate cases while 35(66%) patients were severe/critical cases. Severe/critical patients had higher CT scores in several types of abnormalities than moderate patients from the second week to the fourth week post symptom onset. Overall lung involvement score in the second week demonstrated predictive value for severity with a sensitivity of 81.0% and specificity of 69.2%.

CONCLUSIONS

Our modified semi-quantitative CT scoring system for COVID-19 patients demonstrated feasibility. Overall lung involvement score on the second week had predictive value for clinical severity and could be indicator for further treatment.

COVID-19 and its Mimics: What the Radiologist Needs to Know

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current outbreak of Coronavirus disease 2019 (COVID-19). Although imaging should not be used for first-line screening or diagnosis, radiologists need to be aware of its imaging features, and those of common conditions that may mimic COVID-19 pneumonia. In this Pictorial Essay, we review frequently encountered conditions with imaging features that overlap with those that are typical of COVID-19 (including other viral pneumonias, chronic eosinophilic pneumonia, and organizing pneumonia), and those with features that are indeterminate for COVID-19 (including hypersensitivity pneumonitis, pneumocystis pneumonia, diffuse alveolar hemorrhage, pulmonary edema, and pulmonary alveolar proteinosis).

Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study

Background and Aims Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19). Methods A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed. Results Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p = 0.019), mixed ground-glass opacities (GGOs) and consolidation (p < 0.001), pleural effusion (p < 0.001), and intralesional traction bronchiectasis (p = 0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p < 0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71 ± 6.26 versus 4.86 ± 3.52, p < 0.001), with an area under the curve of 0.91 (0.88-0.94) for COVID-19 detection. Conclusions Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC = 0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia

BACKGROUND

Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP).

METHODS

A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves.

RESULTS

Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance.

CONCLUSIONS

Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Role of computed tomography in COVID-19

Coronavirus disease 2019 (COVID-19) has become a rapid worldwide pandemic. While COVID-19 primarily manifests as an interstitial pneumonia and severe acute respiratory distress syndrome, severe involvement of other organs has been documented. In this article, we will review the role of non-contrast chest computed tomography in the diagnosis, follow-up and prognosis of patients affected by COVID-19 pneumonia with a detailed description of the imaging findings that may be encountered. Given that patients with COVID-19 may also suffer from coagulopathy, we will discuss the role of CT pulmonary angiography in the detection of acute pulmonary embolism. Finally, we will describe more advanced applications of CT in the differential diagnosis of myocardial injury with an emphasis on ruling out acute coronary syndrome and myocarditis.

SARS-CoV-2 (COVID-19): Beginning to Understand a New Virus

Within the last two decades, several members of the Coronaviridae family demonstrated epidemic potential. In late 2019, an unnamed genetic relative, later named SARS-CoV-2 (COVID-19), erupted in the highly populous neighborhoods of Wuhan, China. Unchecked, COVID-19 spread rapidly among interconnected communities and related households before containment measures could be enacted. At present, the mortality rate of COVID-19 infection worldwide is 6.6%. In order to mitigate the number of infections, restrictions or recommendations on the number of people that can gather in a given area have been employed by governments worldwide. For governments to confidently lift these restrictions as well as counter a potential secondary wave of infections, alternative medications and diagnostic strategies against COVID-19 are urgently required. This review has focused on these issues.

COVID-19 or non-COVID viral pneumonia: How to differentiate based on the radiologic findings?

Influenza viruses were responsible for most adult viral pneumonia. Presently, coronavirus disease 2019 (COVID-19) has evolved into serious global pandemic. COVID-19 outbreak is expected to persist in months to come that will be synchronous with the influenza season. The management, prognosis, and protection for these two viral pneumonias differ considerably and differentiating between them has a high impact on the patient outcome. Reverse transcriptase polymerase chain reaction is highly specific but has suboptimal sensitivity. Chest computed tomography (CT) has a high sensitivity for detection of pulmonary disease manifestations and can play a key-role in diagnosing COVID-19. We reviewed 47 studies and delineated CT findings of COVID-19 and influenza pneumonia. The differences observed in the chest CT scan can be helpful in differentiation. For instance, ground glass opacities (GGOs), as the most frequent imaging finding in both diseases, can differ in the pattern of distribution. Peripheral and posterior distribution, multilobular distribution, pure or clear margin GGOs were more commonly reported in COVID-19, whereas central or peri-bronchovascular GGOs and pure consolidations were more seen in influenza A (H1N1). In review of other imaging findings, further differences were noticed. Subpleural curvilinear lines, sugar melted sign, intra-lesional vascular enlargement, reverse halo sign, and fibrotic bands were more reported in COVID-19 than H1N1, while air space nodule, tree-in-bud, bronchiectasia, pleural effusion, and cavitation were more seen in H1N1. This delineation, when combined with clinical manifestations and laboratory results may help to differentiate these two viral infections.

COVID-19 mimics on chest CT: a pictorial review and radiologic guide

Chest imaging is often used as a complementary tool in the evaluation of coronavirus disease 2019 (COVID-19) patients, helping physicians to augment their clinical suspicion. Despite not being diagnostic for COVID-19, chest CT may help clinicians to isolate high suspicion patients with suggestive imaging findings. However, COVID-19 findings on CT are also common to other pulmonary infections and non-infectious diseases, and radiologists and point-of-care physicians should be aware of possible mimickers. This state-of-the-art review goal is to summarize and illustrate possible etiologies that may have a similar pattern on chest CT as COVID-19. The review encompasses both infectious etiologies, such as non-COVID viral pneumonia, Mycoplasma pneumoniae, Pneumocystis jiroveci, and pulmonary granulomatous infectious, and non-infectious disorders, such as pulmonary embolism, fat embolism, cryptogenic organizing pneumonia, non-specific interstitial pneumonia, desquamative interstitial pneumonia, and acute and chronic eosinophilic pneumonia.

From Radiological Manifestations to Pulmonary Pathogenesis of COVID-19: A Bench to Bedside Review

In this review, we aim to assess previous radiologic studies in COVID-19 and suggest a pulmonary pathogenesis based on radiologic findings. Although radiologic features are not specific and there is heterogeneity in symptoms and radiologic and clinical manifestation, we suggest that the dominant pattern of computed tomography is consistent with limited pneumonia, followed by interstitial pneumonitis and organizing pneumonia.

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2

OBJECTIVES

To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia.

METHODS

For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia.

RESULTS

The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.

CONCLUSIONS

The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making.

KEY POINTS

• A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose

With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models.

Social Distancing with Portable Chest Radiographs During the COVID-19 Pandemic: Assessment of Radiograph Technique and Image Quality Obtained at 6 Feet and Through Glass

PURPOSE

To develop a technique that allows portable chest radiography to be performed through the glass door of a patient's room in the emergency department.

MATERIALS AND METHODS

A retrospective review of 100 radiographs (50 [mean age 59.4 ± 17.3, range 22-87; 30 women] performed with the modified technique in April 2020, randomized with 50 [mean age 59 ± 21.6, range 19-100; 31 men] using the standard technique was completed by three thoracic radiologists to assess image quality. Radiation exposure estimates to patient and staff were calculated. A survey was created and sent to 32 x-ray technologists to assess their perceptions of the modified technique. Unpaired Ttests were used for numerical data. A P value < .05 was considered statistically significant.

RESULTS

The entrance dose for a 50th percentile patient was the same between techniques, measuring 169 µGy. The measured technologist exposure from the modified technique assuming a 50th percentile patient and standing 6 feet to the side of the glass was 0.055 µGy, which was lower than standard technique technologist exposure of 0.088 µGy. Of the 100 portable chest radiographs evaluated by three reviewers, two reviewers rated all images as having diagnostic quality, while the other reviewer believed two of the standard images and one of the modified technique images were non-diagnostic. A total of 81% (26 of 32) of eligible technologists completed the survey. Results showed acceptance of the modified technique with the majority feeling safer and confirming conservation of PPE. Most technologists did not feel the modified technique was more difficult to perform.

CONCLUSIONS

The studies acquired with the new technique remained diagnostic, patient radiation doses remained similar, and technologist dose exposure were decreased with modified positioning. Perceptions of the new modified technique by frontline staff were overwhelmingly positive.

Review of the Chest CT Differential Diagnosis of Ground-Glass Opacities in the COVID Era

Coronavirus disease 2019 (COVID-19), a recently emerged lower respiratory tract illness, has quickly become a pandemic. The purpose of this review is to discuss and differentiate typical imaging findings of COVID-19 from those of other diseases, which can appear similar in the first instance. The typical CT findings of COVID-19 are bilateral and peripheral predominant ground-glass opacities. As per the Fleischner Society consensus statement, CT is appropriate in certain scenarios, including for patients who are at risk for and/or develop clinical worsening. The probability that CT findings represent COVID-19, however, depends largely on the pretest probability of infection, which is in turn defined by community prevalence of infection. When the community prevalence of COVID-19 is low, a large gap exists between positive predictive values of chest CT versus those of reverse transcriptase polymerase chain reaction. This implies that with use of chest CT there are a large number of false-positive results. Imaging differentiation is important for management and isolation purposes and for appropriate disposition of patients with false-positive CT findings. Herein the authors discuss differential pathology with close imaging resemblance to typical CT imaging features of COVID-19 and highlight CT features that may help differentiate COVID-19 from other conditions.

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

Background

Since the beginning of 2020, coronavirus disease has spread widely all over the world and this required rapid adequate management; therefore, continuous searching for rapid and sensitive CT chest techniques was needed to give a hand for the clinician.

We aimed to assess the validity of computed tomography (CT) quantitative and qualitative analysis in COVID-19 pneumonia and how it can predict the disease severity on admission.

Results

One hundred and twenty patients were enrolled in our study, 98 (81.7%) of them were males, and 22 (18.3%) of them were females with a mean age of 52.63 ± 12.79 years old, ranging from 28 to 83 years. Groups B and C showed significantly increased number of involved lung segments and lobes, frequencies of consolidation, crazy-paving pattern, and air bronchogram. The total lung severity score and the total score for crazy-paving and consolidation are used as severity indicators in the qualitative method and could differentiate between groups B and C and group A (90.9% sensitivity, 87.5% specificity, and 93.2% sensitivity, 87.5% specificity, respectively), while the quantitative indicators could differentiate these three groups. Using the quantitative CT indicators, the validity to differentiate different groups showed 84.1% sensitivity and 81.2% specificity for the opacity score, and 90.9% sensitivity and 81.2% specificity for the percentage of high opacity.

Conclusion

Advances in CT COVID-19 pneumonia assessment provide an accurate and rapid tool for severity assessment, helping for decision-making notably for the critical cases.

Recent updates on COVID-19: A holistic review

Coronaviruses are large positive-sense RNA viruses with spike-like peplomers on their surface. The Coronaviridae family's strains infect different animals and are popularly associated with several outbreaks, namely SARS and MERS epidemic. COVID-19 is one such recent outbreak caused by SARS-CoV-2 identified first in Wuhan, China. COVID-19 was declared a pandemic by WHO on 11th March 2020. Our review provides information covering various facets of the disease starting from its origin, transmission, mutations in the virus to pathophysiological changes in the host upon infection followed by diagnostics and possible therapeutics available to tackle the situation. We have highlighted the zoonotic origin of SARS-CoV-2, known to share 96.2% nucleotide similarity with bat coronavirus. Notably, several mutations in SARS-CoV-2 spike protein, nucleocapsid protein, PLpro, and ORF3a are reported across the globe. These mutations could alter the usual receptor binding function, fusion process with the host cell, virus replication, and the virus's assembly. Therefore, studying these mutations could help understand the virus's virulence properties and design suitable therapeutics. Moreover, the aggravated immune response to COVID-19 can be fatal. Hypertension, diabetes, and cardiovascular diseases are comorbidities substantially associated with SARS-CoV-2 infection. The review article discusses these aspects, stating the importance of various comorbidities in disease outcomes. Furthermore, medications' unavailability compels the clinicians to opt for atypical drugs like remdesivir, chloroquine, etc. The current diagnostics of COVID-19 include qRT-PCR, CT scan, serological tests, etc. We have described these aspects to expose the information to the scientific community and to accelerate the research.

[Frequency of chest X-ray suggestive of SARS-CoV-2 involvement from March to May 2020 in the population of an urban health area]

OBJECTIVE

Know the number and percentage of chest X-rays (CXR) referred to a Primary Care Imaging Center and Primary Care Emergency Center to rule out lung involvement due to SARS-CoV-2 from March 16 to May 15, 2020, in an urban health area of about 400,000 reference population inhabitants. To determine the percentage of cases suggestive of pulmonary involvement due to SARS-CoV-2 CXR and the percentage of cases without pulmonary involvement of the total CXR derived in the reference population from March 16 to May 15, 2020.

MATERIAL AND METHODS

Design observational descriptive study. The radiological criteria to classify probable pulmonary infection by SARS-CoV-2 (RxT[+]) are: 1) focal opacity; 2) faint focal opacity; 3) faint diffuse increase in density; 4) focal or diffuse interstitial pattern, and 5) focal or diffuse interstitial alveolus pattern.

RESULTS AND CONCLUSIONS

Maintain CXR as a useful screening method in the middle stages of the disease, when CXR is more sensitive to detect lung involvement due to SARS-CoV-2. Our graph of affectation by SARS-CoV-2 does not present assessable differences with the expected curve in an epidemic.

CT chest findings in patients infected with COVID-19: review of literature

Background

Coronavirus disease 2019 (COVID-19) is a highly infectious disease causing severe respiratory distress syndrome that was first discovered by the end of 2019 in Wuhan, China.

Main text

A wide variety of CT findings in COVID-19 have been reported in different studies, and the CT findings differ according to the stage of the disease and disease severity and associated co-morbidities. We will discuss each sign separately and its importance in diagnosis and prognosis.

Conclusion

CT plays a pivotal role in the diagnosis and management of COVID-19 pneumonia. The typical appearance of COVID-19 pneumonia is bilateral patchy areas of ground glass infiltration, more in the lower lobes. The appearance of other signs like consolidation, air bronchogram, crazy pavement appearance, and air bubble signs appear during the course of the disease. In the context of pandemic, the CT chest can be used as a screening tool in symptomatic patients as it is cheaper, available, and time saving.

COVID-19 pneumonia: the great radiological mimicker

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread worldwide since December 2019. Although the reference diagnostic test is a real-time reverse transcription-polymerase chain reaction (RT-PCR), chest-computed tomography (CT) has been frequently used in diagnosis because of the low sensitivity rates of RT-PCR. CT findings of COVID-19 are well described in the literature and include predominantly peripheral, bilateral ground-glass opacities (GGOs), combination of GGOs with consolidations, and/or septal thickening creating a "crazy-paving" pattern. Longitudinal changes of typical CT findings and less reported findings (air bronchograms, CT halo sign, and reverse halo sign) may mimic a wide range of lung pathologies radiologically. Moreover, accompanying and underlying lung abnormalities may interfere with the CT findings of COVID-19 pneumonia. The diseases that COVID-19 pneumonia may mimic can be broadly classified as infectious or non-infectious diseases (pulmonary edema, hemorrhage, neoplasms, organizing pneumonia, pulmonary alveolar proteinosis, sarcoidosis, pulmonary infarction, interstitial lung diseases, and aspiration pneumonia). We summarize the imaging findings of COVID-19 and the aforementioned lung pathologies that COVID-19 pneumonia may mimic. We also discuss the features that may aid in the differential diagnosis, as the disease continues to spread and will be one of our main differential diagnoses some time more.

Contribution of CT Features in the Diagnosis of COVID-19

The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO). As this is a novel virus, its diagnosis is crucial to clinical treatment and management. To date, real-time reverse transcription-polymerase chain reaction (RT-PCR) has been recognized as the diagnostic criterion for COVID-19. However, the results of RT-PCR can be complemented by the features obtained in chest computed tomography (CT). In this review, we aim to discuss the diagnosis and main CT features of patients with COVID-19 based on the results of the published literature, in order to enhance the understanding of COVID-19 and provide more detailed information regarding treatment.

Chest CT in COVID-19 pneumonia: what are the findings in mid-term follow-up?

Purpose

The purposes of this study are to investigate mid-term chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) pneumonia, assess the rate of complete resolution, and determine the individuals at risk for residual abnormalities.

Methods

Fifty-two cases of COVID-19 pneumonia with at least two chest CTs and mean 3-month interval between the initial and follow-up CT were enrolled in this retrospective study. Patients were categorized into two groups: complete resolution and residual disease on follow-up CT. Demographic, clinical, laboratory, and therapeutic data as well as initial and follow-up chest CT scans were compared and analyzed.

Results

Thirty patients (57.7%) demonstrate complete resolution of pulmonary findings, and 22 patients (42.3%) had residual disease on follow-up CT. The mean time interval between initial and follow-up CT was 91.3 ± 17.2 and 90.6 ± 14.3 days in the complete resolution and residual groups, respectively. The most common radiologic pattern in residual disease was ground-glass opacities (54.5%), followed by mixed ground-glass and subpleural parenchymal bands (31.8%), and pure parenchymal bands (13.7%). Compared to complete resolution group, patients with residual disease had higher CT severity score on initial exam (10.3 ± 5.4 vs. 7.3 ± 4.6, P value = 0.036), longer duration of hospitalization, higher rate of intensive care unit (ICU) admission, more underlying medical conditions, higher initial WBC count, and higher occurrence rate of leukocytosis in the hospitalization time period (all P values < 0.05).

Conclusion

Extensive lung involvement on initial CT, ICU admission, long duration of hospitalization, presence of underlying medical conditions, high initial WBC count, and development of leukocytosis during the course of disease are associated with more prevalence of chronic lung sequela of COVID-19.

Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2

OBJECTIVES

To investigate whether meaningful subgroups sharing the CT features of patients with COVID-19 pneumonia could be identified using latent class analysis (LCA) and explore the relationship between the LCA-derived subgroups and clinical types.

METHODS

This retrospective review included 499 patients with confirmed COVID-19 pneumonia between February 11 and March 8, 2020. Subgroups sharing the CT features were identified using LCA. Univariate and multivariate logistic regression models were utilized to analyze the association between clinical types and the LCA-derived subgroups.

RESULTS

Two radiological subgroups were identified using LCA. There were 228 subjects (45.69%) in class 1 and 271 subjects (54.31%) in class 2. The CT findings of class 1 were smaller pulmonary infection volume, more peripheral distribution, more GGO, more maximum lesion range ≤ 5 cm, a smaller number of lesions, less involvement of lobes, less air bronchogram, less dilatation of vessels, less hilar and mediastinal lymph node enlargement, and less pleural effusion than the CT findings of class 2. Univariate analysis demonstrated that older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters associated with an increased risk for class 2. Multivariate analyses revealed that the patients with clinically severe type disease had a 1.97-fold risk of class 2 than the patients with clinically moderate-type disease.

CONCLUSIONS

The demographic and clinical differences between the two radiological subgroups based on the LCA were significantly different. Two radiological subgroups were significantly associated with clinical moderate and severe types.

KEY POINTS

• Two radiological subgroups were identified using LCA. • Older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters with an increased risk for class 2 defined by LCA. • Patients with clinically severe type had a 1.97-fold higher risk of class 2 defined by LCA in comparison with patients showing clinically moderate-type disease.

The recent challenges of highly contagious COVID-19, causing respiratory infections: Symptoms, diagnosis, transmission, possible vaccines, animal models, and immunotherapy

COVID-19 is highly contagious pathogenic viral infection initiated from Wuhan seafood wholesale market of China on December 2019 and spread rapidly around the whole world due to onward transmission. This recent outbreak of novel coronavirus (CoV) was believed to be originated from bats and causing respiratory infections such as common cold, dry cough, fever, headache, dyspnea, pneumonia, and finally Severe Acute Respiratory Syndrome (SARS) in humans. For this widespread zoonotic virus, human-to-human transmission has resulted in nearly 83 lakh cases in 213 countries and territories with 4,50,686 deaths as on 19 June 2020. This review presents a report on the origin, transmission, symptoms, diagnosis, possible vaccines, animal models, and immunotherapy for this novel virus and will provide ample references for the researchers toward the ongoing development of therapeutic agents and vaccines and also preventing the spread of this disease.

The recent challenges of highly contagious COVID-19, causing respiratory infections: Symptoms, diagnosis, transmission, possible vaccines, animal models, and immunotherapy

COVID-19 is highly contagious pathogenic viral infection initiated from Wuhan seafood wholesale market of China on December 2019 and spread rapidly around the whole world due to onward transmission. This recent outbreak of novel coronavirus (CoV) was believed to be originated from bats and causing respiratory infections such as common cold, dry cough, fever, headache, dyspnea, pneumonia, and finally Severe Acute Respiratory Syndrome (SARS) in humans. For this widespread zoonotic virus, human-to-human transmission has resulted in nearly 83 lakh cases in 213 countries and territories with 4,50,686 deaths as on 19 June 2020. This review presents a report on the origin, transmission, symptoms, diagnosis, possible vaccines, animal models, and immunotherapy for this novel virus and will provide ample references for the researchers toward the ongoing development of therapeutic agents and vaccines and also preventing the spread of this disease.

Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms

OBJECTIVE

To analyze the most frequent radiographic features of COVID-19 pneumonia and assess the effectiveness of chest X-ray (CXR) in detecting pulmonary alterations.

MATERIALS AND METHODS

CXR of 240 symptomatic patients (70% male, mean age 65 ± 16 years), with SARS-CoV-2 infection confirmed by RT-PCR, was retrospectively evaluated. Patients were clustered in four groups based on the number of days between symptom onset and CXR: group A (0-2 days), 49 patients; group B (3-5), 75 patients; group C (6-9), 85 patients; and group D (> 9), 31 patients. Alteration's type (reticular/ground-glass opacity (GGO)/consolidation) and distribution (bilateral/unilateral, upper/middle/lower fields, peripheral/central) were noted. Statistical significance was tested using chi-square test.

RESULTS

Among 240 patients who underwent CXR, 180 (75%) showed alterations (group A, 63.3%; group B, 72%; group C, 81.2%; group D, 83.9%). GGO was observed in 124/180 patients (68.8%), reticular alteration in 113/180 (62.7%), and consolidation in 71/180 (39.4%). Consolidation was significantly less frequent (p < 0.01). Distribution among groups was as follows: reticular alteration (group A, 70.9%; group B, 72.2%; group C, 57.9%; group D, 46.1%), GGO (group A, 67.7%; group B, 62.9%; group C, 71%; group D, 76.9%), and consolidation (group A, 35.5%; group B, 31.4%; group C, 47.8%; group D, 38.5%). Alterations were bilateral in 73.3%. Upper, middle, and lower fields were involved in 36.7%, 79.4%, and 87.8%, respectively. Lesions were peripheral in 49.4%, central in 11.1%, or both in 39.4%. Upper fields and central zones were significantly less involved (p < 0.01).

CONCLUSIONS

The most frequent lesions in COVID-19 patients were GGO (intermediate/late phase) and reticular alteration (early phase) while consolidation gradually increased over time. The most frequent distribution was bilateral, peripheral, and with middle/lower predominance. Overall rate of negative CXR was 25%, which progressively decreased over time.

KEY POINTS

• The predominant lung changes were GGO and reticular alteration, while consolidation was less frequent. • The typical distribution pattern was bilateral, peripheral, or both peripheral and central and involved predominantly the lower and middle fields. • Chest radiography showed lung abnormalities in 75% of patients with confirmed SARS-CoV-2 infection, range varied from 63.3 to 83.9%, respectively, at 0-2 days and > 9 days from the onset of symptoms.

Chest CT in COVID-19: What the Radiologist Needs to Know

Chest CT has a potential role in the diagnosis, detection of complications, and prognostication of coronavirus disease 2019 (COVID-19). Implementation of appropriate precautionary safety measures, chest CT protocol optimization, and a standardized reporting system based on the pulmonary findings in this disease will enhance the clinical utility of chest CT. However, chest CT examinations may lead to both false-negative and false-positive results. Furthermore, the added value of chest CT in diagnostic decision making is dependent on several dynamic variables, most notably available resources (real-time reverse transcription-polymerase chain reaction [RT-PCR] tests, personal protective equipment, CT scanners, hospital and radiology personnel availability, and isolation room capacity) and the prevalence of both COVID-19 and other diseases with overlapping manifestations at chest CT. Chest CT is valuable to detect both alternative diagnoses and complications of COVID-19 (acute respiratory distress syndrome, pulmonary embolism, and heart failure), while its role for prognostication requires further investigation. The authors describe imaging and managing care of patients with COVID-19, with topics including (a) chest CT protocol, (b) chest CT findings of COVID-19 and its complications, (c) the diagnostic accuracy of chest CT and its role in diagnostic decision making and prognostication, and (d) reporting and communicating chest CT findings. The authors also review other specific topics, including the pathophysiology and clinical manifestations of COVID-19, the World Health Organization case definition, the value of performing RT-PCR tests, and the radiology department and personnel impact related to performing chest CT in COVID-19. ©RSNA, 2020.

Imaging of lung pathology in COVID-19 (literature review and own data)

Novel coronavirus infection is predominantly manifests as lung tissue damage. Imaging methods, particularly, chest X-Ray and computed tomography, are of great importance for detecting pulmonary changes and differentiate them with other diseases (mainly other viral pneumonias). In the early disease stages the disease presents on CT with ground glass opacities, consolidations, crazy paving symptom. With time course, they can gradually decrease, evolve into organizing pneumonia or stay stable and even increase in volume with the spread of consolidation and formation of several signs of organizing pneumonia. Although radiological methods show high sensitivity in the detection of pulmonary changes, their specificity and prognostic ability are not so good today. Novel coronavirus infection can be complicated with pulmonary embolism, development thrombosisin situin pulmonary small vessels, acute heart failure and subsequent development of cardiogenic pulmonary edema, bacterial superinfection, exacerbation or worsening of chronic lung disease and several iatrogenic issues (pneumothorax, pneumomediastinum, hematomas).

Evaluation of chest CT and clinical features of COVID-19 patient in Macao

Purpose

Coronavirus disease-19 (COVID-19) was firstly reported in December 2019 in Wuhan, China and soon after, the number of cases increased rapidly worldwide. As of May 2, 2020, more than 3,000,000 confirmed cases have been reported world. In Macao, there were 45 confirmed COVID-19 cases until May 2, 2020.

Material and Methods

In this study, we summarized the radiological features of these cases and analyzed relationship between the clinical characteristics and radiological findings. We retrospectively analyzed the imaging manifestations of the 45 cases with COVID-19 in Macao, focusing on identifying and characterizing the most common radiological findings of COVID-19. We retrospectively analyze the relationship between the clinical features and radiological finding of COVID-19 pneumonia.

Results

This study showed that chest CT manifestations of COVID-19 were multiple ground-glass densities in both lungs. It is dominated by bilateral peripheral subpleural distribution, which may be accompanied by consolidation, interlobular septa thickening, and adjacent pleura thickening. As the disease progresses, it can manifest as consolidation of the lungs in CT scan. We also found the age, smoking and hypertension may be risk factor for predicting the severity of COVID-19 in radiology.

Conclusion

COVID-19 should be diagnosed based on the clinical feature, nCoV PCR test and radiological manifestation. The main manifestation of COVID-19 is peripheral ground glass opacity. Age, smoking and hypertension may be used to predict the severity of COVID-19. Chest CT is the important radiological method for screening and detecting COVID-19 patients.

Chest computed tomography findings of coronavirus disease 2019 (COVID-19) pneumonia

OBJECTIVE

To retrospectively analyze the chest computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

From January 9, 2020, to February 26, 2020, totally 56 laboratory-confirmed patients with COVID-19 underwent chest CT. For 40 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and the affected lung lobes. Furthermore, the initial CT scan and the follow-up CT scans were compared.

RESULTS

Forty patients (83.6%) had two or more opacities in the lung. Eighteen (32.7%) patients had only ground-glass opacities; twenty-nine patients (52.7%) had ground-glass and consolidative opacities; and eight patients (14.5%) had only consolidation. A total of 43 patients (78.2%) showed two or more lobes involved. The opacities tended to be both in peripheral and central (30/55, 54.5%) or purely peripheral distribution (25/55, 45.5%). Fifty patients (90.9%) had the lower lobe involved. The first follow-up CT scans showed that twelve patients (30%) had improvement, 26 (65%) patients had mild-moderate progression, and two patients (5%) had severe progression with "white lungs." The second follow-up CT showed that 22 patients (71%) showed improvement compared with the first follow-up CT, four patients (12.9%) had aggravated progression, and five patients (16.1%) showed unchanged radiographic appearance.

CONCLUSIONS

The common CT features of COVID-19 pneumonia are multiple lung opacities, multiple types of the opacity (ground-glass, ground-glass and consolidation, and consolidation alone), and multiple lobes especially the lower lobe involved. Follow-up CT could demonstrate the rapid progression of COVID-19 pneumonia (either in aggravation or absorption).

KEY POINTS

• The predominant CT features of COVID-19 pneumonia are multiple ground-glass opacities with or without consolidation and, with both lungs, multiple lobes and especially the lower lobe affected. • CT plays a crucial role in early diagnosis and assessment of COVID-19 pneumonia progression. • CT findings of COVID-19 pneumonia may not be consistent with the clinical symptoms or the initial RT-PCR test results.

Chest X-ray findings monitoring COVID-19 disease course and severity

Background

Coronavirus related respiratory illness usually manifests clinically as pneumonia with predominant imaging findings of an atypical or organizing pneumonia. Plain radiography is very helpful for COVID-19 disease assessment and follow-up. It gives an accurate insight into the disease course.

We aimed to determine the COVID-19 disease course and severity using chest X-ray (CXR) scoring system and correlate these with patients’ age, sex, and outcome.

Results

In our study, there were 350 patients proven with positive COVID-19 disease; 220 patients (62.9%) had abnormal baseline CXR and 130 patients (37.1%) had normal baseline CXR. During follow-up chest X-ray studies, 48 patients (13.7%) of the normal baseline CXR showed CXR abnormalities. In abnormal chest X-ray, consolidation opacities were the most common finding seen in 218 patients (81.3%), followed by reticular interstitial thickening seen in 107 patients (39.9%) and GGO seen in 87 patients (32.5%). Pulmonary nodules were found 25 patients (9.3%) and pleural effusion was seen in 20 patients (7.5%). Most of the patients showed bilateral lung affection (181 patients, 67.5%) with peripheral distribution (156 patients, 58.2%) and lower zone affection (196 patients, 73.1%). The total severity score was estimated in the baseline and follow-up CXR and it was ranged from 0 to 8. The outcome of COVID-19 disease was significantly related to the age, sex, and TSS of the patients. Male patients showed significantly higher mortality rate as compared to the female patients (P value 0.025). Also, the mortality rate was higher in patients older than 40 years especially with higher TSS.

Conclusion

Radiographic findings are very good predictors for assessing the course of COVID-19 disease and it could be used as long-term consequences monitoring.

Temporal changes of CT findings between non-severe and severe cases of COVID-19 pneumonia: a multi-center, retrospective, longitudinal Study

Background and aim: To perform a longitudinal analysis of serial CT findings over time in patients with COVID-19 pneumonia. Methods: From February 5 to March 8, 2020, 73 patients (male to female, ratio of 43:30; mean age, 51 years) with COVID-19 pneumonia were retrospectively enrolled and followed up until discharge from three institutions in China. The patients were divided into the severe and non-severe groups according to treatment option. The patterns and distribution of lung abnormalities, total CT scores, single ground-glass opacity (GGO) CT scores, single consolidation CT scores, single reticular CT scores and the amounts of zones involved were reviewed by 2 radiologists. These features were analyzed for temporal changes. Results: In non-severe group, total CT scores (median, 9.5) and the amounts of zones involved were slowly increased and peaked in disease week 2. In the severe group, the increase was faster, with scores also peaking at 2 weeks (median, 20). In both groups, the later parameters began to decrease in week 4 (median values of 9 and 19 in the non-severe and severe groups, respectively). In the severe group, the dominant residual lung lesions were reticular (median single reticular CT score, 10) and consolidation (median single consolidation CT score, 7). In the non-severe group, the dominant residual lung lesions were GGO (median single GGO CT score, 7) and reticular (median single reticular CT score, 4). In both non-severe and severe groups, the GGO pattern was dominant in week 1, with a higher proportion in the severe group compared with the non-severe group (72% vs. 65%). The consolidation pattern peaked in week 2, with 9 (32%) and 19 (73%) in the non-severe and severe groups, respectively; the reticular pattern became dominant from week 4 (both group >40%). Conclusion: The extent of CT abnormalities in the severe and non-severe groups peaked in disease week 2. The temporal changes of CT manifestations followed a specific pattern, which might indicate disease progression and recovery.

Atypical Chest Computed Tomography Finding of Predominant Interstitial Thickening in a Patient with Coronavirus Disease 2019 (COVID-19) Pneumonia

Patient: Male, 77-year-old

Final Diagnosis: COVID-19 pneumonia

Symptoms: Cough • shortness of breath

Medication:—

Clinical Procedure: —

Specialty: Radiology

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia.

Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4–89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern). Clinical findings were compared across different categories. Time-dependent progression of CT patterns and correlations with clinical outcomes, i.e.„ discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed.

Results: Of 94 patients with outcome, 81 (86.2%) were discharged, 3 (3.2%) were admitted to ICU, 4 (4.3%) required mechanical ventilation, 6 (6.4%) died. 31 (38.3%) had complete absorption at median day 37 after symptom onset. Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P < 0.05). Remarkable evolution was observed in Pattern 0–2 and Pattern 3–4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0–3 [reference]; hazard-ratio [95% CI], 18.90 [1.91–186.60], P = 0.012]. CT pattern [Pattern 3–4 vs. Pattern 0–2 [reference]; 0.26 [0.08–0.88], P = 0.030] and C-reactive protein [>10 vs. ≤ 10 mg/L [reference]; 0.31 [0.13–0.72], P = 0.006] were risk factors associated with pulmonary residuals.

Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia.

Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia

OBJECTIVES

To investigate the different CT characteristics which may distinguish influenza from 2019 coronavirus disease (COVID-19).

METHODS

A total of 13 confirmed patients with COVID-19 were enrolled from January 16, 2020, to February 25, 2020. Furthermore, 92 CT scans of confirmed patients with influenza pneumonia, including 76 with influenza A and 16 with influenza B, scanned between January 1, 2019, to February 25, 2020, were retrospectively reviewed. Pulmonary lesion distributions, number, attenuation, lobe predomination, margin, contour, ground-glass opacity involvement pattern, bronchial wall thickening, air bronchogram, tree-in-bud sign, interlobular septal thickening, intralobular septal thickening, and pleural effusion were evaluated in COVID-19 and influenza pneumonia cohorts.

RESULTS

Peripheral and non-specific distributions in COVID-19 showed a markedly higher frequency compared with the influenza group (p < 0.05). Most lesions in COVID-19 showed balanced lobe localization, while in influenza pneumonia they were predominantly located in the inferior lobe (p < 0.05). COVID-19 presented a clear lesion margin and a shrinking contour compared with influenza pneumonia (p < 0.05). COVID-19 had a patchy or combination of GGO and consolidation opacities, while a cluster-like pattern and bronchial wall thickening were more frequently seen in influenza pneumonia (p < 0.05). The lesion number and attenuation, air bronchogram, tree-in-bud sign, interlobular septal thickening, and intralobular septal thickening were not significantly different between the two groups (all p > 0.05).

CONCLUSIONS

Though viral pneumonias generally show similar imaging features, there are some characteristic CT findings which may help differentiating COVID-19 from influenza pneumonia.

KEY POINTS

• CT can play an early warning role in the diagnosis of COVID-19 in the case of no epidemic exposure. • CT could be used for the differential diagnosis of influenza and COVID-19 with satisfactory accuracy. • COVID-19 had a patchy or combination of GGO and consolidation opacities with peripheral distribution and balanced lobe predomination.

Changes in CT manifestations and RT-PCR testings of the coronavirus disease 2019 until recovery in patients with afferent infection vs. second-generation infection outside the original city (Wuhan): An observational study

OBJECTIVE

To investigate changes in CT manifestations and results of reverse transcription polymerase chain reaction (RT-PCR) testing between afferent and second-generation coronavirus disease 2019 (COVID-19) outside the original city (Wuhan) until recovery.

METHODS

We collected 26 consecutive COVID-19 patients undergoing initial and follow-up CT scans together with RT-PCR until recovery from 2 hospitals outside the original city. Seventeen patients with afferent infection and 9 with second-generation infection were assigned to Group A and B, respectively. By observing CT manifestations, we scored COVID-19, and statistically analyzed numbers of patients with changes in CT scores and RT-PCR results between stages.

RESULTS

The total score of COVID-19 on initial CT manifestations was higher in Group A than in Group B (P < 0.05). COVID-19 progressed more frequently from stage 1-2, and relieved from stage 3-4 in Group A (P < 0.05). The similar trend in Group A could not be found in Group B. Results of RT-PCR in most of patients in Group A turned negative at stage 4 while those in Group B turned negative at stage 3 (P < 0.05).

CONCLUSION

Changes in CT manifestation and RT-PCR result can be different between afferent and second-generation COVID-19 until recovery.

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab

Objectives

We aimed to assess the diagnostic performance of CT in patients with a negative first RT-PCR testing and to identify typical features of COVID-19 pneumonia that can guide diagnosis in this case.

Methods

Patients suspected of COVID-19 with a negative first RT-PCR testing were retrospectively revalued after undergoing CT.

CT was reviewed by two radiologists and classified as suspected COVID-19 pneumonia, non-COVID-19 pneumonia or negative.

The performance of both first RT-PCR result and CT was evaluated by using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) and by using the second RT-PCR test as the reference standard.

CT findings for confirmed COVID-19 positive or negative were compared by using the Pearson chi-squared test (P values < 0.05)

Results

Totally, 337 patients suspected of COVID-19 underwent CT and nasopharyngeal swabs in March 2020. Eighty-seven out of 337 patients had a negative first RT-PCR result; of these, 68 repeated RT-PCR testing and were included in the study.

The first RT-PCR test showed SE 0, SP = 100%, PPV = NaN, NPV = 70%, AUC = 50%, and CT showed SE = 70% SP = 79%, PPV = 86%, NPV = 76%, AUC = 75%.

The most relevant CT variables were ground glass opacity more than 50% and peripheral and/or perihilar distribution.

Discussion

Negative RT-PCR test but positive CT features should be highly suggestive of COVID-19 in a cluster or community transmission scenarios, and the second RT-PCR test should be promptly requested to confirm the final diagnosis.

2019 novel coronavirus (COVID-19) pneumonia: CT manifestations and pattern of evolution in 110 patients in Jiangxi, China

OBJECTIVES

To elucidate the CT manifestations and patterns of evolution in patients with COVID-19 pneumonia.

METHODS

This is a retrospective review of CT scans of 110 patients. All 110 patients had initial and second CT scans, 60 of 110 patients had three serial CT scans, and 17 of 60 patients had four serial CT scans. Numerous characteristics of pulmonary and extrapulmonary abnormalities and recognizable patterns of evolution were evaluated.

RESULTS

Of the 110 initial CT scans, ground-glass opacities without consolidation (65.4%) were more common than a consolidation or mixed pattern. The most common findings were subpleural involvement (77.2%), multifocal involvement (80.7%), and bilateral involvement (67.3%). Three serial CT scans of 60 patients showed four patterns of CT evolution: type 1 showing relatively high CT scores on initial CT (averaged 4 days after symptom onset), with mild progression and improvement on follow-up CT scans (25%); type 2 with progression of CT findings from initial CT to first follow-up CT (averaged 9 days after symptom onset) with subsequent improvement on second follow-up CT (averaged 13 days after symptom onset, 61.7%); type 3 with no CT changes (5.0%); and type 4 pattern was similar to type 2 but with a more prolonged course and more severe CT findings (8.3%).

CONCLUSIONS

Predominant findings at initial CT scans were bilateral multifocal subpleural GGO. The most commonly shown evolution pattern was type 2: progression of disease with increased extent and density of opacities on first follow-up CT followed by improvement on second follow-up CT.

KEY POINTS

• Predominant findings at initial CT in patients with COVID-19 infection are bilateral multifocal subpleural ground-glass opacities. • Ill-defined patchy or nodular opacities were most commonly observed on CT scans with the right lower lobe most commonly involved. • The most commonly shown evolution pattern on chest CT was type 2: progression of CT findings from initial CT to first follow-up CT with subsequent improvement on second follow-up CT.

Chest complications in immunocompromised patients without acquired immunodeficiency syndrome (AIDS): differentiation between infectious and non-infectious diseases using high-resolution CT findings

AIM

To differentiate between infectious and non-infectious diseases occurring in immunocompromised patients without acquired immunodeficiency syndrome (AIDS) using high-resolution computed tomography (HRCT).

MATERIALS AND METHODS

HRCT images of 555 patients with chest complications were reviewed retrospectively. Infectious diseases (n=341) included bacterial pneumonia (n=123), fungal infection (n=80), septic emboli (n=11), tuberculosis (n=15), pneumocystis pneumonia (n=101), and cytomegalovirus pneumonia (n=11), while non-infectious diseases (n=214) included drug toxicity (n=84), infiltration of underlying diseases (n=83), idiopathic pneumonia syndrome (n=34), diffuse alveolar haemorrhage (n=8), and pulmonary oedema (n=5). Lung parenchymal abnormalities were compared between the two groups using the χ² test and multiple logistic regression analysis.

RESULTS

The χ2 test results showed significant differences in many HRCT findings between the two groups. Multiple logistic regression analysis results indicated the presence of nodules with a halo and the absence of interlobular septal (ILS) thickening were the significant indicators that could differentiate infectious from non-infectious diseases. ILS thickening was generally less frequent among most infectious diseases and more frequent among most non-infectious diseases, with a good odds ratio (7.887, p<0.001). The sensitivity and accuracy for infectious diseases in the absence of ILS thickening were better (70% and 73%, respectively) than those of nodules with a halo (19% and 48%, respectively), while the specificity in the nodules with a halo was better (93%) than that of ILS thickening (78%).

CONCLUSIONS

The presence of nodules with a halo or the absence of ILS thickening tends to suggest infectious disease. Specifically, ILS thickening seems to be a more reliable indicator.

The "pandemic" increase in lung ultrasound use in response to Covid-19: can we complement computed tomography findings? A narrative review

Coronavirus disease of 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has rapidly spread to a global pandemic in March 2020. This emergency condition has been putting a severe strain on healthcare systems worldwide, and a prompt, dynamic response is instrumental in its management. While a definite diagnosis is based on microbiological evidence, the relationship between lung ultrasound (LU) and high-resolution computed tomography (HRCT) in the diagnosis and management of COVID-19 is less clear. Lung ultrasound is a point-of-care imaging tool that proved to be useful in the identification and severity assessment of different pulmonary conditions, particularly in the setting of emergency and critical care patients in intensive care units; HRCT of the thorax is regarded as the mainstay of imaging evaluation of lung disorders, enabling characterization and quantification of pulmonary involvement. Aims of this review are to describe LU and chest HRCT main imaging features of COVID-19 pneumonia, and to provide state-of-the-art insights regarding the integrated role of these techniques in the clinical decision-making process of patients affected by this infectious disease.

Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques

In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one. Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts' opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination.

Chest CT findings of COVID-19-infected patients, are there differences between pediatric and adult patients? A systematic review

Background

Purpose of this study was to deliver a report of chest CT findings of COVID-19-infected pediatric and adult patients and to make an age-based comparison. A systematic search was conducted in accordance with PRISMA guidelines to identify relevant studies in the electronic databases of PubMed, Scopus, ProQuest, ScienceDirect, and Web of Sciences from January 1, 2020 to March 27, 2020 using search terms in the titles and abstracts. Based on our inclusion and exclusion criteria, 762 articles were screened. Finally, 15 eligible articles which had adequate data on chest CT findings of COVID-19-infected patients were enrolled in this systematic review.

Results

In pediatric patients (15 years old or younger), peripheral distribution was found in 100% of cases, ground glass opacities (GGO) in 55.2%, bilateral involvement in 50%, halo sign in 50%, unilateral involvement in 30%, consolidation in 22.2%, crazy paving pattern in 20%, nodular opacities in 15%, pleural effusion in 4.2%, lymphadenopathy in none, and normal imaging in 20.8% of cases. On the other hand, in adult patients, bilateral involvement was reported in 76.8%, GGO in 68.4%, peripheral distribution in 62.2%, mixed GGO and consolidation in 48.7%, consolidation in 33.7%, crazy paving pattern in 27.7%, mixed central and peripheral distribution in 25.0%, unilateral involvement in 15.2%, nodular opacities in 9.2%, pleural effusion in 5.5%, central distribution of lesions in 5.4%, lymphadenopathy in 2.4%, and normal imaging in 9.8% of cases.

Conclusion

According to the findings of this systematic review, children infected with COVID-19 can present with normal or atypical findings (nodular opacities/unilateral involvement) in chest imaging more frequently than adult patients. Therefore, more caution should be taken to avoid misdiagnosis or missed diagnosis in infected children. Besides, clinical and laboratory findings need to be considered more decision-making for pediatric patients with normal or atypical chest CT scan but high suspicion of COVID-19.

Chest CT-based differential diagnosis of 28 patients with suspected corona virus disease 2019 (COVID-19)

OBJECTIVES

The chest CT findings that can distinguish patients with corona virus disease 2019 (COVID-19) from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative have not previously been described in detail. The purpose of this study was to determine the distinctions among patients with COVID-19 by comparing the imaging findings of patients with suspected confirmed COVID-19 and those of patients initially suspected to have COVID-19 who were ultimately negative for the disease.

METHODS

28 isolated suspected in-patients with COVID-19 were enrolled in this retrospective study from January 22, 2020 to February 6, 2020. 12 patients were confirmed to have positive severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) RNA results, and 16 patients had negative results. The thin-section CT imaging findings and clinical and laboratory data of all the patients were evaluated.

RESULTS

There were no significant differences between the 12 confirmed COVID-19 (SARS-Cov-2-positive) patients and 16 SARS-CoV-2-negative patients in epidemiology and most of the clinical features or laboratory data. The CT images showed that the incidence of pure/mixed ground-glass opacities (GGOs) was not different between COVID-19 and SARS-CoV-2-negative patients [9/12 (75.0%) vs 10/16 (62.5%), p = 0.687], but pure/mixed GGOs in the peripheral were more common in patients with COVID-19 [11/12 (91.7%) vs 6/16 (37.5%), p = 0.006]. There were no significant differences in the number of lesions, bilateral lung involvement, large irregular/patchy opacities, rounded opacities, linear opacities, crazy-paving patterns, halo signs, interlobular septal thickening or air bronchograms.

CONCLUSIONS

Although peripheral pure/mixed GGOs on CT may help distinguish patients with COVID-19 from clinically suspected but negative patients, CT cannot replace RT-PCR testing.

ADVANCES IN KNOWLEDGE

Peripheral pure/mixed GGOs on-chest CT findings can be helpful in distinguishing patients with COVID-19 from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative.

Coronavirus Disease 2019 (COVID-19): A Perspective from China

In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, Hubei Province, China, and spread across China and beyond. On February 12, 2020, the World Health Organization officially named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). Because most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns, radiologic examinations have become vital in early diagnosis and the assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19 while highlighting the role of chest CT in prevention and disease control.

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score

OBJECTIVE

To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19).

MATERIALS AND METHODS

We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods.

RESULTS

There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score (p = 0.001).

CONCLUSION

The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

Chest X-ray features of SARS-CoV-2 in the emergency department: a multicenter experience from northern Italian hospitals

OBJECTIVES

To evaluate the imaging features of routine admission chest X-ray in patients referred for novel Coronavirus 2019 infection.

METHODS

All patients referred to the emergency departments, RT-PCR positive for SARS-CoV-2 infection were evaluated. Demographic and clinical data were recorded. Two radiologists (8 and 15 years of experience) reviewed all the X-ray images and evaluated the following findings: interstitial opacities, alveolar opacities (AO), AO associated with consolidation, consolidation and/or pleural effusion. We stratified patients in groups according to the time interval between symptoms onset (cut-off 5 days) and X-ray imaging and according to age (cut-off 60 years old). Computed tomography was performed in case of a discrepancy between clinical symptoms, laboratory and X-ray findings, and/or suspicion of complications.

RESULTS

A total of 468 patients were tested positive for SARS-CoV-2. Lung lesions primarily manifested as interstitial opacities (71.7%) and AO opacities (60.5%), more frequently bilateral (64.5%) and with a peripheral predominance (62.5%). Patients admitted to the emergency radiology department after 5 days from symptoms onset, more frequently had interstitial and AO opacities, in comparison to those admitted within 5 days, and lung lesions were more frequently bilateral and peripheral. Older patients more frequently presented interstitial and AO opacities in comparison to younger ones. Sixty-eight patients underwent CT that principally showed the presence of ground-glass opacities and consolidations.

CONCLUSIONS

The most common X-ray pattern is multifocal and peripheral, associated with interstitial and alveolar opacities. Chest X-ray, compared to CT, can be considered a reliable diagnostic tool, especially in the Emergency setting.

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

Coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, has rapidly swept around the world just within a month, causing global public health emergency. In diagnosis, chest computed tomography (CT) manifestations can supplement parts of limitations of real-time reverse transcription polymerase chain reaction (RT-PCR) assay. Based on a comprehensive literature review and the experience in the frontline, we aim to review the typical and relatively atypical CT manifestations with representative COVID-19 cases at our hospital, and hope to strengthen the recognition of these features with radiologists and help them make a quick and accurate diagnosis.Key Points• Ground glass opacities, consolidation, reticular pattern, and crazy paving pattern are typical CT manifestations of COVID-19.• Emerging atypical CT manifestations, including airway changes, pleural changes, fibrosis, nodules, etc., were demonstrated in COVID-19 patients.• CT manifestations may associate with the progression and prognosis of COVID-19.

Clinical imaging characteristics of inpatients with coronavirus disease-2019 in Heilongjiang Province, China: a retrospective study

Objective: To investigate the clinical, laboratory, and radiological characteristics of patients with coronavirus disease-2019 (COVID-19) in Heilongjiang Province.

Results: Patients in the ICU group were older and their incidence of cardiovascular disease was higher than those in the non-ICU group. Lymphocyte levels were lower and neutrophil and D-dimer levels were higher in the ICU than that in the non-ICU group. Compared to the non-ICU group, the incidence of pulmonary consolidation and ground-glass opacity with consolidation was significantly higher in the ICU group, all lung lobes were more likely to be involved, with higher number of lung lobes and areas surrounding the bronchi. Of the 59 patients with COVID-19 in this group, 15 received mechanical ventilation. All intubated patients involved lung lobes, and a large number of lesions were observed in the area around the bronchial vessels.

Conclusion: Significant differences were observed in clinical symptoms, laboratory tests, and computed tomography features between the ICU and non-ICU groups.

Methods: A total of 59 patients with COVID-19, comprising 44 patients in the intensive care unit (ICU) and 15 in the non-ICU, were retrospectively analyzed. Characteristics of the two groups of patients were compared.

The Role of Imaging Techniques in Management of COVID-19 in China: From Diagnosis to Monitoring and Follow-Up

In December 2019, an outbreak of coronavirus infection emerged in Wuhan, Hubei Province of China, which is now named Coronavirus Disease 2019 (COVID-19). The outbreak spread rapidly within mainland China and globally. This paper reviews the different imaging modalities used in the diagnosis and treatment process of COVID-19, such as chest radiography, computerized tomography (CT) scan, ultrasound examination, and positron emission tomography (PET/CT) scan. A chest radiograph is not recommended as a first-line imaging modality for COVID-19 infection due to its lack of sensitivity, especially in the early stages of infection. Chest CT imaging is reported to be a more reliable, rapid, and practical method for diagnosis of COVID-19, and it can assess the severity of the disease and follow up the disease time course. Ultrasound, on the other hand, is portable and involves no radiation, and thus can be used in critically ill patients to assess cardiorespiratory function, guide mechanical ventilation, and identify the presence of deep venous thrombosis and secondary pulmonary thromboembolism. Supplementary information can be provided by PET/CT. In the absence of vaccines and treatments for COVID-19, prompt diagnosis and appropriate treatment are essential. Therefore, it is important to exploit the advantages of different imaging modalities in the fight against COVID-19.

Different computed tomography patterns of Coronavirus Disease 2019 (COVID-19) between survivors and non-survivors

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R2 = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R2 = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.