COVID-19: Abdominal and Pelvic Imaging Findings: A Primer for Radiologists.

ABSTRACT: The COVID-19 pandemic presents an ongoing global health threat. The SARS-CoV-2 is known to cause substantial pulmonary disease, and most of the current radiological publications are dedicated to describing and characterizing these findings. However, studies regarding imaging findings in the abdomen and pelvis of infected patients are still very limited. The aim of this review is to discuss the most frequent abdominal manifestations based on the current literature and representative images from our local experience.

Systematic Screening of COVID-19 Disease Based on Chest CT and RT-PCR for Cancer Patients Undergoing Radiation Therapy in a Coronavirus French Hotspot.

PURPOSE: Patients with cancer are presumed to be more vulnerable to COVID-19. We evaluated a screening strategy combining chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) for patients treated with radiation therapy at our cancer center located in a COVID-19 French hotspot during the first wave of the pandemic. METHODS AND MATERIALS: Chest CT images were proposed during radiation therapy CT simulation. Images were reviewed by an expert radiologist according to the COVID-19 Reporting and Data System classification. Nasal swabs with RT-PCR assay were initially proposed in cases of suspicious imaging or clinical context and were eventually integrated into the systematic screening. A dedicated radiation therapy workflow was proposed for COVID-19 patients to limit the risk of contamination. RESULTS: From March 18, 2020 to May 1, 2020, 480 patients were screened by chest CT, and 313 patients had both chest CT and RT-PCR (65%). The cumulative incidence of COVID-19 was 5.4% (95% confidence interval [CI], 3.6-7.8; 26 of 480 patients). Diagnosis of COVID-19 was made before radiation therapy for 22 patients (84.6%) and during RT for 4 patients (15.3%). Chest CT directly aided the diagnosis of 7 cases in which the initial RT-PCR was negative or not feasible, out of a total of 480 patients (1.5%) and 517 chest CT acquisitions. Four patients with COVID-19 at the time of the chest CT screening had a false negative CT. Sensitivity and specificity of chest CT screening in patients with both RT-PCR and chest CT testing were estimated at 0.82 (95% CI, 0.60-0.95) and 0.98 (95% CI, 0.96-0.99), respectively. Adaptation of the radiation therapy treatment was made for all patients, with 7 postponed treatments (median: 5 days; interquartile range, 1.5-14.8). CONCLUSIONS: The benefit of systematic use of chest CT screening during CT simulation for patients undergoing radiation therapy during the COVID-19 pandemic seemed limited.

Container CT scanner: a solution for modular emergency radiology department during the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic period, container computed tomography (CT) scanners were developed and used for the first time in China to perform CT examinations for patients with clinically mild to moderate COVID-19 who did not need to be hospitalized for comprehensive treatment, but needed to be isolated in Fangcang shelter hospitals (also known as makeshift hospitals) to receive some supportive treatment. The container CT is a multidetector CT scanner installed within a radiation-protected stand-alone container (a detachable lead shielding room) that is deployed outside the makeshift hospital buildings. The container CT approach provided various medical institutions with the solution not only for rapid CT installation and high adaptability to site environments, but also for significantly minimizing the risk of cross-infection between radiological personnel and patients during CT examination in the pandemic. In this article, we described the typical setup of a container CT and how it worked for chest CT examinations in Wuhan city, the epicenter of COVID-19 outbreak.

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification .

Frequency and characterization of ancillary chest CT findings in COVID-19 pneumonia.

OBJECTIVES: Ground-glass opacity and consolidation are recognized typical features of Coronavirus disease-19 (COVID-19) pneumonia on Chest CT, yet ancillary findings have not been fully described. We aimed to describe ancillary findings of COVID-19 pneumonia on CT, to define their prevalence, and investigate their association with clinical data. METHODS: We retrospectively reviewed our CT chest cases with coupled reverse transcriptase polymerase chain reaction (rt-PCR). Patients with negative rt-PCR or without admission chest CT were excluded. Ancillary findings included: vessel enlargement, subpleural curvilinear lines, dependent subpleural atelectasis, centrilobular solid nodules, pleural and/or pericardial effusions, enlarged mediastinal lymph nodes. Continuous data were expressed as median and 95% confidence interval (95% CI) and tested by Mann-Whitney U test. RESULTS: Ancillary findings were represented by 106/252 (42.1%, 36.1 to 48.2) vessel enlargement, 50/252 (19.8%, 15.4 to 25.2) subpleural curvilinear lines, 26/252 (10.1%, 7.1 to 14.7) dependent subpleural atelectasis, 15/252 (5.9%, 3.6 to 9.6) pleural effusion, 15/252 (5.9%, 3.6 to 9.6) mediastinal lymph nodes enlargement, 13/252 (5.2%, 3 to 8.6) centrilobular solid nodules, and 6/252 (2.4%, 1.1 to 5.1) pericardial effusion. Air space disease was more extensive in patients with vessel enlargement or centrilobular solid nodules (p < 0.001). Vessel enlargement was associated with longer history of fever (p = 0.035) and lower admission oxygen saturation (p = 0.014); dependent subpleural atelectasis with lower oxygen saturation (p < 0.001) and higher respiratory rate (p < 0.001); mediastinal lymph nodes with shorter history of cough (p = 0.046); centrilobular solid nodules with lower prevalence of cough (p = 0.023), lower oxygen saturation (p < 0.001), and higher respiratory rate (p = 0.032), and pericardial effusion with shorter history of cough (p = 0.015). Ancillary findings associated with longer hospital stay were subpleural curvilinear lines (p = 0.02), whereas centrilobular solid nodules were associated with higher rate of intensive care unit admission (p = 0.01). CONCLUSION: Typical high-resolution CT findings of COVID-19 pneumonia are frequently associated with ancillary findings that variably associate with disease extent, clinical parameters, and disease severity. ADVANCES IN KNOWLEDGE: Ancillary findings might reflect the broad range of heterogeneous mechanisms in severe acute respiratory syndrome from viral pneumonia, and potentially help disease phenotyping.

Analysis of Chest CT Results of Coronavirus Disease 2019 (COVID-19) Patients at First Follow-Up.

Objective: To investigate the dissipation and outcomes of pulmonary lesions at the first follow-up of patients who recovered from moderate and severe cases of COVID-19. Methods: From January 21 to March 3, 2020, a total of 136 patients with COVID-19 were admitted to our hospital. According to inclusion and exclusion criteria, 52 patients who recovered from COVID-19 were included in this study, including 33 moderate cases and 19 severe cases. Three senior radiologists independently and retrospectively analyzed the chest CT imaging data of 52 patients at the last time of admission and the first follow-up after discharge, including primary manifestations, concomitant manifestations, and degree of residual lesion dissipation. Results: At the first follow-up after discharge, 16 patients with COVID-19 recovered to normal chest CT appearance, while 36 patients still had residual pulmonary lesions, mainly including 33 cases of ground-glass opacity, 5 cases of consolidation, and 19 cases of fibrous strip shadow. The proportion of residual pulmonary lesions in severe cases (17/19) was statistically higher than in moderate cases (19/33) (χ 2 = 5.759, P < 0.05). At the first follow-up, residual pulmonary lesions were dissipated to varying degrees in 47 cases, and lesions remained unchanged in 5 cases. There were no cases of increased numbers of lesions, enlargement of lesions, or appearance of new lesions. The dissipation of residual pulmonary lesions in moderate patients was statistically better than in severe patients (Z = -2.538, P < 0.05). Conclusion: Clinically cured patients with COVID-19 had faster dissipation of residual pulmonary lesions after discharge, while moderate patients had better dissipation than severe patients. However, at the first follow-up, most patients still had residual pulmonary lesions, which were primarily ground-glass opacity and fibrous strip shadow. The proportion of residual pulmonary lesions was higher in severe cases of COVID-19, which required further follow-up.

Myocarditis: imaging up to date.

Myocarditis is an inflammatory disease of the heart muscle, diagnosed by histological, immunological, and immunohistochemical criteria. Endomyocardial biopsy represents the diagnostic gold standard for its diagnosis but is infrequently used. Due to its noninvasive ability to detect the presence of myocardial edema, hyperemia and necrosis/fibrosis, Cardiac MR imaging is routinely used in the clinical practice for the diagnosis of acute myocarditis. Recently pixel-wise mapping of T1 and T2 relaxation time have been introduced into the clinical Cardiac MR protocol increasing its accuracy. Our paper will review the role of MR imaging in the diagnosis of acute myocarditis.

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

BACKGROUND Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, SARS-CoV-2, and is associated with severe respiratory disease. There are extensive publications on the chest computed tomography (CT) findings of COVID-19 pneumonia, with ground-glass opacities (GGO) and mixed GGO and consolidation being the most common findings. Those with interstitial thickening manifesting as reticular opacities typically show superimposed ground-glass opacities, giving a crazy-paving pattern. CASE REPORT We report the case of a 77-year-old man with a background of asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS) who presented with progressive cough and shortness of breath for 2 days. He was in close contact with a confirmed COVID-19 case. Reverse-transcription polymerase chain reaction analysis of a nasopharyngeal swab was positive for SARS-CoV-2. The initial chest radiograph was negative for lung consolidation and ground-glass opacities. During admission, he had worsening shortness of breath with desaturation, prompting a chest CT examination, which was performed on day 14 of illness. The chest CT revealed an atypical finding of predominant focal subpleural interstitial thickening in the right lower lobe. He was provided supportive treatment along with steroid and antibiotics. He recovered well and subsequently tested negative for 2 consecutive swabs. He was discharged after 34 days. CONCLUSIONS Interstitial thickening or reticular pattern on CT has been described in COVID-19 pneumonia, but largely in association with ground-glass opacity or consolidation. This case demonstrates an atypical predominance of interstitial thickening on chest CT in COVID-19 pneumonia on day 14 of illness, which is the expected time of greatest severity of the disease.

Usefulness of Mobile Computed Tomography in Patients with Coronavirus Disease 2019 Pneumonia: A Case Series.

The coronavirus disease (COVID-19) outbreak has reached global pandemic status as announced by the World Health Organization, which currently recommends reverse transcription polymerase chain reaction (RT-PCR) as the standard diagnostic tool. However, although the RT-PCR test results may be found negative, there are cases that are found positive for COVID-19 pneumonia on computed tomography (CT) scan. CT is also useful in assessing the severity of COVID-19 pneumonia. When clinicians desire a CT scan of a patient with COVID-19 to monitor treatment response, a safe method for patient transport is necessary. To address the engagement of medical resources necessary to transport a patient with COVID-19, our institution has implemented the use of mobile CT. Therefore, we report two cases of COVID-19 pneumonia evaluated by using mobile cone-beam CT. Although mobile cone-beam CT had some limitations regarding its image quality such as scatter noise, motion and streak artifacts, and limited field of view compared with conventional multi-detector CT, both cases had acceptable image quality to establish the diagnosis of COVID-19 pneumonia. We report the usefulness of mobile cone-beam CT in patients with COVID-19 pneumonia.

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

Incidental diagnosis of Covid-19 pneumonia on chest computed tomography.

PURPOSE: The purpose of this study was to determine the prevalence and imaging characteristics of incidentally diagnosed COVID-19 pneumonia on computed tomography (CT). MATERIALS AND METHODS: This retrospective study was conducted between March 20th and March 31st, 2020 at Cochin hospital, Paris France. Thoracic CT examinations of all patients referred for another reason than a suspicion of SARS-CoV-2 infection were reviewed. CT images were analyzed by a chest radiologist to confirm the presence of findings consistent with COVID-19 pneumonia and quantify disease extent. Clinical and biological data (C-reactive protein serum level [CRP] and white blood cell count) of patients with CT findings suggestive for COVID-19 pneumonia were retrieved from the electronic medical chart. RESULTS: During the study period, among 205 diagnostic CT examinations, six examinations (6/205, 3%) in 6 different patients (4 men, 2 women; median age, 57 years) revealed images highly suggestive of COVID-19 pneumonia. The final diagnosis was confirmed by RT-PCR. Three inpatients were suspected of extra thoracic infection whereas three outpatients were either fully asymptomatic or presented with fatigue only. All had increased CRP serum level and lymphopenia. Disease extent on CT was mild to moderate in 5/6 patients (83%) and severe in 1/6 patient (17%). CONCLUSION: Cumulative incidence of fortuitous diagnosis if COVID-19 pneumonia did not exceed 3% during the highest pandemic phase and was predominantly associated with limited lung involvement.

Effects of Corticosteroid Treatment for Non-Severe COVID-19 Pneumonia: A Propensity Score-Based Analysis.

BACKGROUND AND OBJECTIVE: The effects of corticosteroid treatment on non-severe COVID-19 pneumonia patients are unknown. To determine the impacts of adjuvant corticosteroid administrated to patients with non-severe COVID-19 pneumonia. METHOD: A retrospective cohort study based on propensity score analysis was designed to explore the effects of corticosteroid on several clinical outcomes. RESULTS: One hundred thirty-two patients satisfied the inclusion criteria and 35 pairs were generated according to propensity score matching. Compared to non-corticosteroid group, the CT score on day 7 was significantly higher in corticosteroid group (8.6 (interquartile range [IQR], 2.8-11.5) versus 12.0 (IQR, 5.0-19.3), P = 0.046). In corticosteroid group, more patients progressed to severe cases (11.4% versus 2.9%, P = 0.353), hospital stay (23.5 days (IQR, 19-29 d) versus 20.2 days (IQR, 14-25.3 d), P = 0.079) and duration of viral shedding (20.3 days (IQR, 15.2-24.8 d) versus 19.4 days (IQR, 11.5-28.3 d), P = 0.669) were prolonged, while fever time (9.5 days (IQR, 6.5-12.2 d) versus 10.2 days (IQR, 6.8-14 d), P = 0.28) was shortened; however, all these data revealed no statistically significant differences. CONCLUSION: Corticosteroid might have a negative effect on lung injury recovery in non-severe COVID-19 pneumonia patients; however, the results of this study must be interpreted with caution because of confounding factors.

Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics.

OBJECTIVE: To explore the value of CT texture analysis (CTTA) for determining coronavirus disease 2019 (COVID-19) severity. METHODS: The clinical and CT data of 81 patients with COVID-19 were retrospectively analyzed. The texture features were extracted using LK2.1. The two-sample t test or Mann-Whitney U test was used to find the significant features. Minimum redundancy and maximum relevance (MRMR) method was performed to find the features with maximum correlation and minimum redundancy. These features were then used to construct a radiomics texture model to discriminate the severe patients using multivariate logistic regression method. Besides, a clinical model was also built. ROC analyses were conducted to evaluate the performance of two models. The correlations of clinical features and textural features were analyzed using the Spearman correlation analysis. RESULTS: Of the total cases included, 60 were common and 21 were severe. (1) For textural features, 20 radiomics features selected by MRMR showed good performance in discriminating the two groups (AUC > 70%). (2) For clinical features, chi-square tests or Mann-Whitney U tests identified 16 clinical features as significant, and 12 were discriminative (p < 0.05) between two groups analyzed by univariate logistic analysis. Of these, 10 had an AUC > 70%. (3) Prediction models for textural features and clinical features were established, and both showed high predictive accuracy. The AUC values of textural features and clinical features were 0.93 (0.86-1.00) and 0.95 (0.95-0.99), respectively. (4) The Spearman correlation analysis showed that most textural and clinical features had above-moderate correlations with disease severity (> 0.4). CONCLUSION: Texture analysis can provide reliable and objective information for differential diagnosis of COVID-19. KEY POINTS: • CT texture analysis can well differentiate common and severe COVID-19 patients. • Some textural features showed above-moderate correlations with clinical factors. • CT texture analysis can provide useful information to judge the severity of COVID-19.

Imaging Features of Coronavirus disease 2019 (COVID-19): Evaluation on Thin-Section CT.

RATIONALE AND OBJECTIVES: To retrospectively analyze the chest imaging findings in patients with coronavirus disease 2019 (COVID-19) on thin-section CT. MATERIALS AND METHODS: Fifty-three patients with confirmed COVID-19 infection underwent thin-section CT examination. Two chest radiologists independently evaluated the imaging in terms of distribution, ground-glass opacity (GGO), consolidation, air bronchogram, stripe, enlarged mediastinal lymph node, and pleural effusion. RESULTS: Fourty-seven cases (88.7%) had findings of COVID-19 infection, and the other six (11.3%) were normal. Among the 47 cases, 78.7% involved both lungs, and 93.6% had peripheral infiltrates distributed along the subpleural area. All cases showed GGO, 59.6% of which were round and 40.4% patchy. Other imaging features included "crazy-paving pattern" (89.4%), consolidation (63.8%), and air bronchogram (76.6%). Air bronchograms were observed within GGO (61.7%) and consolidation (70.3%). Neither enlarged mediastinal lymph nodes nor pleural effusion were present. Thirty-three patients (62.3%) were followed an average interval of 6.2 ± 2.9 days. The lesions increased in 75.8% and resorbed in 24.2% of patients. CONCLUSION: COVID-19 showed the pulmonary lesions in patients infected with COVID-19 were predominantly distributed peripherally in the subpleural area.