Atypical chest CT findings of COVID-19 pneumonia: a pictorial review.

Coronavirus disease 2019 (COVID-19) first emerged in China and rapidly spread in the world causing a pandemic. Chest computed tomography (CT) continues to play an important role in the diagnosis and follow-up of the disease due to shortcomings of the real-time reverse transcription-polymerase chain reaction test, which is the gold standard in the diagnosis of this disease. Typical chest CT findings of COVID-19 pneumonia have been widely reported in the literature. However, atypical findings such as central involvement, peribronchovascular involvement, isolated upper lobe involvement, nodular involvement, lobar consolidation, solitary involvement, pleural and pericardial fluid, and subpleural sparing can also be seen. Knowing these atypical findings is important to avoid misdiagnosis. This review summarizes the atypical findings that can be seen in the course of the disease and may be confused with other diseases.

COVID-19 pneumonia: lessons learned, challenges, and preparing for the future.

Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia.

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

Prevalence of Incidental Gynecomastia by Chest Computed Tomography in Patients with a Prediagnosis of COVID-19 Pneumonia.

OBJECTIVE: In this study, we aimed to determine the prevalence of gynecomastia by evaluating computed tomography (CT) images of male patients who were admitted to our hospital during the coronavirus disease-2019 (COVID-19) pandemic. MATERIALS AND METHODS: This study included a total of 1,877 patients who underwent chest CT for prediagnosis of COVID-19 pneumonia between March 15th and May 15th, 2020. All images were evaluated for the presence of gynecomastia. Gynecomastia patterns were evaluated according to morphological features, and diagnoses were made by measuring the largest glandular tissue diameter. Statistical analysis was performed with IBM SPSS software version 25.0. RESULTS: The prevalence of gynecomastia was 32.3%. In terms of pattern, 22% were nodular, 57% were dendritic, and 21% were diffuse glandular gynecomastia. A significant correlation was found between age and gynecomastia pattern (p<0.001). The incidence of nodular, dendritic, and diffuse glandular gynecomastia increased with advancing age. A significant difference was found in the analysis of the correlation between age groups and glandular tissue diameters (p<0.001). With an increase in glandular tissue diameter, the gynecomastia pattern changed from a nodular to a diffuse glandular pattern. CONCLUSION: In our study, gynecomastia diagnosis was made through axial CT images. Although CT should not replace mammography and ultrasonography for clinical diagnosis of gynecomastia, chest CT scans can be used to evaluate patients with suspected gynecomastia.

COVID-19 radiology CT personnel management.

A new coronavirus outbreak called COVID-19 started in December 2019. In Turkey, the first case was reported on 10 March 2020. In this article, information will be given about the patient and staff management and organization that we have implemented in the Radiology Department of our hospital during the COVID-19 pandemic. The rules we followed were: 1- Performing the examinations of COVID-19 patients and suspects with a CT device isolated from other patients; 2- Reducing the unnecessary workload in imaging modalities other than CT, emergency radiography, and emergency ultrasonography; 3- Directing and managing patients and their relatives in accordance with the mask and distancing rules; 4- Disinfecting the device with an appropriate disinfectant after each patient in order to prevent cross-contamination; 5- Protecting the entire technician team from infection by employing one week work, two weeks off shifts of fixed teams; 6- Ensuring adequate ventilation of the gantry room. Adhering to the above rules, no infection spread was reported from the Radiology department and especially the COVID-19 CT unit.

Radiological findings of COVID-19 pneumonia

Although real-time polymerase chain reaction (RT-PCR) remains the standard reference for a definitive diagnosis of coronavirus disease 2019 (COVID-19) infection, the false-negative rate and the lack of availability of RT-PCR assays in the early stages of the outbreak restricted the prompt diagnosis of infected patients. Since most COVID-19 infected patients were diagnosed with pneumonia based on characteristic imaging patterns, radiological examinations have become vital for early diagnosis and the assessment of disease course. Although chest radiography is not sufficiently sensitive for the detection of small ground-glass opacity (GGO) and may produce normal findings in the early stage of infection, it can be used as an initial imaging method, especially in young patients. Thin-slice chest computed tomography (CT) plays a vital role in early detection, observation, and disease evaluation. Typical CT findings of COVID-19 include peripherally distributed multifocal GGOs with patchy consolidations and predilection for the posterior part or lower lobe involvement. The increasing numbers, extent, and density of GGOs on CT indicate disease progression. Lung involvement progresses to consolidation up to 2 weeks after disease onset. Clear and frequent communication among health-care providers, including radiologists, is vital for the improvement of patient care during this pandemic.

Correlation of liver-to-spleen ratio, lung CT scores, clinical, and laboratory findings of COVID-19 patients with two consecutive CT scans.

PURPOSE: Given the lack of information about abdominal imaging findings and correlation with clinical features of COVID-19, we aimed to evaluate the changes in hepatic attenuation during the course of disease. Our aim was to correlate the liver-to-spleen ratio (L/S), clinical, laboratory findings, and lung CT scores of patients with COVID-19 who had two consecutive chest CTs. METHODS: A retrospective search was performed between March 1, 2020 and April 26, 2020 to identify patients who had positive RT-PCR tests and two unenhanced chest CTs. Scans that were obtained at hospital admission and follow-up were reviewed to assess L/S and lung CT scores. Patients were divided into two groups based on lung CT scores (non-progressive vs progressive). Patient demographics, laboratory findings, length of hospital stay, and survival were noted from electronic medical records. RESULTS: Twenty patients in the progressive group and 7 patients in the non-progressive group were identified. The mean L/S of the progressive group (1.13 ± 0.3) was lower than that of the non-progressive group (1.21 ± 0.29) at hospital admission but there was no significant difference between the two groups (p = 0.547). L/S at follow-up was significantly different between the groups as the mean L/S values of the progressive and non-progressive groups were 1.02 ± 0.23 and 1.25 ± 0.29, respectively (p = 0.009). L/S was negatively correlated with AST and ALT (r = - 0.46, p = 0.016 and r = - 0.534, p = 0.004, respectively). There were significant differences between the two groups in terms of WBC, neutrophil, lymphocyte, monocyte, and platelet counts that were obtained at hospital admission. Length of hospital stay was significantly longer in patients in the progressive group (p = 0.035). CONCLUSIONS: Decrease in L/S may be observed in patients with elevated lung CT scores at follow-up. WBC, neutrophil, lymphocyte, monocyte, and platelet counts at hospital admission may predict the progression of COVID-19.

The novel coronavirus pneumonia (COVID-19): a pictorial review of chest CT features.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in Wuhan, China. The infection rapidly spread to more than 200 countries around the world. The clinical presentation of the disease may vary from mild illness to severe pneumonia such as acute respiratory distress syndrome (ARDS). The chest computed tomography (CT) has an important complementary role in diagnosis of the disease. The predominant CT findings of the disease are ground glass opacities and consolidations located in subpleural areas of lower lobes. Widespread ground-glass opacities, consolidation, air bronchograms, central involvement of lung parenchyma, mediastinal lymphadenopathy are more common in patients with the severe form of the disease. CT imaging also guides in differentiation of alternative diagnosis or in assessment of associated pulmonary embolism during the course of the disease. In this pictorial review we aim to review the CT features of COVID-19 pneumonia and mention the changes throughout the disease process.

CT imaging features of COVID-19 pneumonia: initial experience from Turkey.

PURPOSE: We aimed to demonstrate the computed tomography (CT) findings observed at the initial presentation of coronavirus disease 2019 (COVID-19) pneumonia and reveal the most frequent infiltration and distribution patterns of the disease. METHODS: One hundred and eighty-five patients (87 men, 98 women; mean age, 48.7 years), who underwent RT-PCR sampling and high-resolution CT examination in our hospital between March 15, 2020, and April 15, 2020, and got a definitive diagnosis of COVID-19 disease via initial or follow-up RT-PCR test, were included in the study. We comprehensively analyzed the most common and relatively rare CT imaging features (e.g., distribution pattern, density of the lesions, additional CT signs) in patients diagnosed with COVID-19 pneumonia. RESULTS: Thirty-eight patients (20.6%) had no evidence of pneumonia on their initial high-resolution CT images. Among 147 patients (79.4%) who had parenchymal infiltration consistent with pneumonia, 10 (6.8%) had a negative baseline RT-PCR test, and positivity was detected as a result of repeated tests. Most of the patients had multifocal (89.1%) and bilateral (86.4%) lesions. The most common location, right lower lobe, was affected in 87.8% of the patients. Lesions were distributed predominantly at peripheral (87.1%) and posterior (46.3%) areas of lung parenchyma. Most of the patients had pure ground glass opacity (GGO) (82.3%) followed by GGO with consolidation (32.7%) and crazy paving pattern (21.8%). Pure consolidation, solid nodules, halo sign, reverse halo sign, vascular enlargement, subpleural line, air-bronchogram, and bronchiectasis were the other findings observed in at least 15% of the cases. Halo sign, acinar nodules, air-bubble sign, pleural thickening and effusion, mediastinal and/or hilar lymphadenopathy were seen rarely (2%-12.9%). Pericardial effusion, pneumothorax, cavitation, and tree-in-bud pattern were not detected in our study group. CONCLUSION: Multifocal and bilateral GGO infiltration predominantly distributed in peripheral, posterior, and lower lung areas was the most common infiltration pattern.

Chest CT features of the novel coronavirus disease (COVID-19)

A new type of coronavirus (2019-nCoV) is rapidly spreading worldwide and causes pneumonia, respiratory distress, thromboembolic events, and death. Chest computed tomography (CT) plays an essential role in the diagnosis of viral pneumonia, monitoring disease progression, determination of disease severity, and evaluating therapeutic efficacy. Chest CT can show important clues of 2019-nCoV disease (also known as COVID-19) in patients with an appropriate clinic. Prompt diagnosis of COVID-19 is essential to prevent disease transmission and provides close clinical observation of patients with clinically severe disease. Therefore, radiologists and clinicians should be familiar with the CT imaging findings of COVID-19 pneumonia. Herein, we aimed to review the imaging findings of COVID-19 pneumonia and examine the critical points to be considered for imaging in cases with COVID-19 suspicion.