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.

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.

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.

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.

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.

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.