COVID-19 in Italy: Comparison of CT Findings from Time Zero to the Delta Variant.

On 12 March 2020, the World Health Organization (WHO) declared the novel Coronavirus (CoV) disease a global Pandemic and an emerging risk. In order to understand patterns that are typical in COVID-19 pneumonia and track the evolution of the disease, the role of the chest computed tomography (CT) is pivotal. The impact of the illness as well as the efficiency of the therapy are also monitored carefully when performing this imaging exam. Coronaviruses, specifically CoV-2, as RNA viruses, have a tendency to frequently change their genome, giving the virus beneficial characteristics such as greater transmissibility, pathogenicity and the possibility to escape the previously acquired immunity. Therefore, genome evaluation became an extremely important routine practice worldwide. In particular, in Italy, four variants have been recognised and each of them represent a specific temporal wave of the disease. Hence, our goal was to describe imaging findings of COVID-19 pneumonia, specifically its most typical imaging identified during the period of our study, and to assess whether or not SARS-CoV-2 variants determine different CT patterns. Our analyses revealed that the SARS-CoV-2 genotype seems not to interfere with the severity of CT patterns and, in particular, bilateral Ground Glass Opacities (GGOs) are the most frequent findings in all COVID-19 waves.

Pulmonary interstitial emphysema and complications: incidence and CT findings in COVID-19.

Background and Objectives: There are scant data available in the published literature providing chest computed tomography (CT) findings on pulmonary interstitial emphysema (PIE), complications and associated parenchymal abnormalities. We report the incidence of PIE and complications by chest CT in patients with COVID-19. Methods: We retrospective analyzed 897 chest CT scans performed with 64-slice CT scanners during the COVID-19 pandemic period from March 2020 to September 2021. Two radiologists and two physicians in training in diagnostic radiology, independently and in consensus, assessed PIE as air within the perilobular (low-attenuation area) and perivascular interstitium such as its complications, parenchymal anomalies and pleural effusion; in addition, the complications of PIE, parenchymal anomalies and pleural effusion were evaluated. Descriptive statistics were used to summarize the data, and the results were expressed as counts and percentages. Results: PIE was revealed in 25 out of 897 patients (2.8%) and associated with pneumomediastinum, subcutaneous emphysema, and pneumothorax in 25 (100%), 16 (64%), and 7 (28%), patients, respectively. Out of 25 patients, 24 patients had ground-glass opacity (GGO), 23 patients had crazy paving, 22 patients had consolidation and 2 patients had pleural effusion. Eighteen out of 25 patients had noninvasive ventilation before CT scan, initially treated with continuous positive airway pressure (positive end expiratory pressure [PEEP] of 10 cmH2O) and subsequently with bilevel positive airway pressure (range PEEP of 8-12 cmH2O). The remaining seven patients had invasive mechanical ventilation via orotracheal intubation (pressure plateau at approximately 25 cmH2O). Six out of 25 (24%) patients died. Conclusion: Chest CT allows the detection of complications associated with PIE and parenchyma abnormalities. The timely detection of PIE and minimal pneumomediastinum could aid the optimization of ventilation modalities and parameters based on patients clinical status therefore potentially reducing complications.

Chest CT features of children infected by B.1.617.2 (Delta) variant of COVID-19.

BACKGROUND: This study aimed to explore the imaging characteristics, diversity and changing trend in CT scans of pediatric patients infected with Delta-variant strain by studying imaging features of children infected with Delta and comparing the results to those of children with original COVID-19. METHODS: A retrospective, comparative analysis of initial chest CT manifestations between 63 pediatric patients infected with Delta variant in 2021 and 23 pediatric patients with COVID-19 in 2020 was conducted. Corresponding imaging features were analyzed. In addition, the changing trend in imaging features of COVID-19 Delta-variant cases were explored by evaluating the initial and follow-up CT scans. RESULTS: Among 63 children with Delta-variant COVID-19 in 2021, 34 (53.9%) showed positive chest CT presentation; and their CT score (1.10 ± 1.41) was significantly lower than that in 2020 (2.56 ± 3.5) (P = 0.0073). Lesion distribution: lung lesions of Delta cases appear mainly in the lower lungs on both sides. Most children had single lobe involvement (18 cases, 52.9%), 14 (41.2%) in the right lung alone, and 14 (41.2%) in both lungs. A majority of Delta cases displayed initially ground glass (23 cases, 67.6%) and nodular shadows (13 cases, 38.2%) in the first CT scan, with few extrapulmonary manifestations. The 34 children with abnormal chest CT for the first time have a total of 92 chest CT examinations. These children showed a statistically significant difference between the 0-3 day group and the 4-7 day group (P = 0.0392) and a significant difference between the 4-7 day group and the more than 8 days group (P = 0.0003). CONCLUSIONS: The early manifestations of COVID-19 in children with abnormal imaging are mostly small subpleural nodular ground glass opacity. The changes on the Delta-variant COVID-19 chest CT were milder than the original strain. The lesions reached a peak on CT in 4-7 days and quickly improved and absorbed after a week. Dynamic CT re-examination can achieve a good prognosis.

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.

Vital role of chest CT in diagnosis of coronavirus disease 2019 (COVID-19).

In December 2019, a new virus called coronavirus disease 2019 (COVID-19) causing severe acute respiratory syndrome emerged in Wuhan, China, and rapidly spread to other areas of China and other regions of the world. Since it was a discovery, COVID-19 has spread to several countries and to this date, affecting about 2,329,651 people and caused about 160,721 deaths. Since most COVID-19 infected cases were diagnosed with pneumonia and characteristic chest computed tomography (CT) scan patterns, radiological examinations have become an important tool in early diagnosis. Nowadays, CT findings combined with normal blood cells (WBCs), lymphopenia and a history of epidemiological exposure have been used as criteria for clinical diagnosis of COVID-19. It is noteworthy that reverse transcription polymerase chain reaction (RT-PCR) test is still gold standard for the diagnosis. This review focuses on role of chest CT in the clinical evaluation of disease progression and more accurate diagnosis.

Initial chest CT findings in COVID-19: correlation with clinical features.

In December 2019, coronavirus disease 2019 (COVID-19), a new de novo infectious disease, was first identified in Wuhan, China and quickly spread across China and around the world. The etiology was a novel betacoronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Lu et al., 2020). On Mar. 11, 2020, World Health Organization (WHO) characterized COVID-19 as a global pandemic. As of Mar. 22, 2020, over 292 000 confirmed COVID-19 cases have been reported globally. To date, COVID-19, with its high infectivity, has killed more people than severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) combined (Wu and McGoogan, 2020).