Radiographic and chest CT imaging presentation and follow-up of COVID-19 pneumonia: a multicenter experience from an endemic area

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Purpose

Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis.

Methods

Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes.

Results

This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively).

Conclusions

The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.

Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.

Diagnostic performance of CT lung severity score and quantitative chest CT for stratification of COVID-19 patients

Purpose

Lung severity score (LSS) and quantitative chest CT (QCCT) analysis could have a relevant impact to stratify patients affected by COVID-19 pneumonia at the hospital admission. The study aims to assess LSS and QCCT performances in severity stratification of COVID-19 patients.

Materials and methods

From April 19, 2020, until May 3, 2020, patients with chest CT suggestive for interstitial pneumonia and tested positive for COVID-19 were retrospectively enrolled and stratified for hospital admission as Group 1, 2 and 3 (home isolation, low intensive care and intensive care, respectively). For LSS, lungs were divided in 20 regions and visually assessed by two radiologists who scored for each region from non-lung involvement as 0, < 50% assigned as 1 and > 50% as 2. QCCT was performed with a dedicated software that extracts pulmonary involvement expressed in liters and percentage. LSS and QCCT were analyzed with ROC curve analysis to predict the performance of both methods. P values < 0.05 were considered statistically significant.

Results

Final population enrolled included 136 patients (87 males, mean age 66 ± 16), 19 patients in Group 1, 86 in Group 2 and 31 in Group 3. Significant differences for LSS were observed in almost all comparisons, especially in Group 1 vs 3 (AUC 0.850, P < 0,0001) and Group 1 + 2 vs 3 (AUC 0.783, P < 0,0001). QCCT showed significant results in almost all comparisons, especially between Group 1 vs 3 (AUC 0.869, P < 0,0001). LSS and QCCT comparison between Group 1 and Group 2 did not show significant differences.

Conclusions

LSS and QCCT could represent promising tools to stratify COVID-19 patient severity at the admission.

Cannonball appearance, a tricky yet underrated imaging manifestation of COVID-19

Spreading swiftly across the borders and over the seas, severe acute respiratory syndrome-related coronavirus-2 (SARS-COV-2), as causative pathogen of coronavirus disease 2019 (COVID-19), is currently the main global public health concern. "Cannonball appearance," as a rare and yet underrated CT feature of COVID-19 pneumonia, has been typically linked to certain hematogenous pulmonary metastases and some inflammatory/infection conditions, including tuberculosis, but no other viral or atypical pneumonia. Cannonball appearance can bring diagnostic dilemmas and difficulties in monitoring treatment response in patients with or suspicious for hematogenous pulmonary metastasis. Hereby, we report two cases of COVID-19 delta variant-induced pneumonia manifesting unusually in chest CT scan with cannonball appearance.

Research on SARS-COV-2 pandemic: a narrative review focused on the Italian contribution

Background

Since late 2019, a severe acute respiratory syndrome, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread with overwhelming speed causing over 214 million confirmed infections and more than 4.5 million deaths worldwide. In this framework, Italy had the second highest number of SARS-CoV-2 infections worldwide, and the largest number of deaths. A global effort of both the scientific community and governments has been undertaken to stem the pandemic. The aim of this paper is to perform a narrative review of the Italian contribution to the scientific literature regarding intensive care management of patients suffering from COVID-19, being one of the first western countries to face an outbreak of SARS-CoV-2 infection.

Main body

We performed a narrative review of the literature, dedicating particular attention and a dedicated paragraph to ventilatory support management, chest imaging findings, biomarkers, possible pharmacological interventions, bacterial superinfections, prognosis and non-clinical key aspects such as communication and interaction with relatives.

Conclusions

Many colleagues, nurses and patients died leaving their families alone. To all of them, we send our thoughts and dedicate these pages.

COVID-19 ARDS: a review of imaging features and overview of mechanical ventilation and its complications

The first cluster of cases of COVID-19 pneumonia was reported on December 31, 2019. Since then, this disease has spread rapidly across the world, and as of September 17, 2021, there are 226,844,344 cases of COVID-19 worldwide with 4,666,334 deaths related to COVID-19. While most COVID-19 cases are mild, some cases are severe with patients developing acute respiratory distress syndrome (ARDS). The pathophysiology of ARDS includes damage to the alveolar epithelium that leads to increased permeability of the alveolar epithelial barrier causing hyaline membrane formation, interstitial edema, and alveolar edema that results in severe hypoxia. Patients with COVID-19 ARDS are supported by non-invasive or invasive mechanical ventilation with an aim to improve oxygenation and maintain adequate blood oxygen levels. Increased intra-alveolar pressure while on mechanical ventilation may lead to alveolar rupture and thus barotrauma-related injuries such as lung tension cysts, pulmonary interstitial emphysema (PIE), pneumomediastinum, pneumopericardium, and pneumothorax. Recent studies have shown that the rate of barotrauma-related events is higher in patients with COVID-19 ARDS compared to patients with ARDS secondary to other etiologies. Radiologists should be aware of the imaging features of COVID-19 ARDS as well as the complications of mechanical ventilation. This educational manuscript will review the features of COVID-19 ARDS, discuss imaging of patients on mechanical ventilation, and review the imaging features of complications related to mechanical ventilation, including ventilator-associated lung injuries.

Postacute Sequelae of COVID-19 Pneumonia: 6-month Chest CT Follow-up

Background

The long-term post acute pulmonary sequelae of COVID-19 remain unknown.

Purpose

To evaluate lung injury in patients affected by COVID-19 pneumonia at six-month follow-up compared to baseline chest CT.

Methods

From March 19th,2020 to May 24th,2020, patients with moderate to severe COVID-19 pneumonia and baseline Chest CT were prospectively enrolled at six-months follow-up. CT qualitative findings, semi-quantitative Lungs Severity Score (LSS) and well-aerated lung quantitative Chest CT (QCCT) were analyzed. Baseline LSS and QCCT performances in predicting fibrotic-like changes (reticular pattern and/or honeycombing) at six-month follow-up Chest CT were tested with receiver operating characteristic curves. Univariable and multivariable logistic regression analysis were used to test clinical and radiological features predictive of fibrotic-like changes. The multivariable analysis was performed with clinical parameters alone (clinical model), radiological parameters alone (radiological model) and the combination of clinical and radiological parameters (combined model).

Results

One-hundred-eighteen patients, with both baseline and six-month follow-up Chest CT, were included in the study (62 female, mean age 65±12 years). At follow-up Chest CT, 85/118 (72%) patients showed fibrotic-like changes and 49/118 (42%) showed GGOs. Baseline LSS (>14), QCCT (≤3.75L and ≤80%) showed an excellent performance in predicting fibrotic-like changes at Chest CT follow-up. In the multivariable analysis, AUC was .89 (95%CI .77-.96) for the clinical model, .81 (95%CI .68-.9) for the radiological model and .92 (95%CI .81-.98)for the combined model.

Conclusion

At six-month follow-up Chest CT, 72% of patients showed late sequelae, in particular fibrotic-like changes. Baseline LSS and QCCT of well-aerated lung showed an excellent performance in predicting fibrotic-like changes at six-month Chest CT (AUC>.88). Male sex, cough, lymphocytosis and QCCT well-aerated lung were significant predictors of fibrotic-like changes at six-month with an inverse correlation (AUC .92).

See also the editorial by Wells and Devaraj.

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay

Ultrasound, chest X-ray, and computed tomography (CT) have been used with excellent results in diagnosis, first assessment, and follow-up of COVID-19 confirmed and suspected patients. Ultrasound and chest X-ray have the advantages of the wide availability and acquisition at the patient's bed; CT showed high sensitivity in COVID-19 diagnosis. Ground-glass opacities and consolidation are the main CT and X-ray features; the distribution of lung abnormalities is typically bilateral and peripheral. Less typical findings, including pleural effusion, mediastinal lymphadenopathies, the bubble air sign, and cavitation, can also be visible on chest CT. Radiologists should be aware of the advantages and limitations of the available imaging techniques and of the different pulmonary aspects of COVID-19 infection.

Diagnostic accuracy of chest computed tomography in improving the false negative rate as compared to reverse transcriptase polymerase chain reaction in coronavirus disease 2019 pneumonia: A cross sectional analysis of 348 cases from India

Background:

Early differentiation between emergency department (ED) patients with and without coronavirus disease 2019 (COVID-19) is very important. The diagnosis of COVID-19 depends on real-time reverse transcriptase polymerase chain reaction (RT-PCR). On imaging, computed tomography (CT) manifestations resemble those seen in viral pneumonias, with multifocal ground-glass opacities and consolidation in a peripheral distribution being the most common findings. Although these findings lack specificity for COVID-19 diagnosis on imaging grounds, CT could be used to provide objective assessment about the extension of the lung opacities, which could be used as an imaging surrogate for disease burden. We set out to investigate the diagnostic accuracy of chest CT scanning in detecting COVID-19 in a population with suspected COVID-19 patients.

Materials and Methods:

In this cross-sectional single-center study performed on 348 cases with clinical suspicion of COVID-19, all adult symptomatic ED patients had both a CT scan and a PCR upon arrival at. CT results were compared with PCR test (s) and diagnostic accuracy was calculated.

Results:

Between February 15, 2020, and June 30, 2020, 348 symptomatic patients were included. In total, 62.3% of patients had a positive PCR and 69.8% had a positive CT, resulting in a sensitivity of 94.2%, specificity of 76.7%, likelihood ratio (LR) of +2.94 and (LR) −0.18. The sensitivity of the CT tended to be higher in those with acute respiratory distress syndrome (100.0%, P = 0.017) and severe COVID-19 (98.5%, P = 0.027) than in patients with mild (82.5%, P = 0.047) and moderate COVID-19 (89.3%, P = 0.039). The diagnostic ability of chest CT was found to be high with 86.3% concordance between findings of CT and PCR. In 48 (13.7%) patients, discordant findings between CT and PCR were observed. In most cases, the CT scan was considered suspicious for COVID-19, while the PCR was negative (37/48, 77.0%). In the majority of these, the diagnosis at discharge was pulmonary infection (n = 26; 54.1%).

Conclusion:

The accuracy of chest CT in symptomatic COVID suspect patients is high, but when used as a single diagnostic test, CT cannot accurately diagnose or exclude COVID-19. Therefore, we recommend a combination of both CT and RT-PCR for future follow-up, management, and medical surveillance.

Multidisciplinary Approach to the Diagnosis and In-Hospital Management of COVID-19 Infection: A Narrative Review

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 or COVID-19 disease) was declared a pandemic on 11th March 2020 by the World Health Organization. This unprecedented circumstance has challenged hospitals' response capacity, requiring significant structural and organizational changes to cope with the surge in healthcare demand and to minimize in-hospital risk of transmission. As our knowledge advances, we now understand that COVID-19 is a multi-systemic disease rather than a mere respiratory tract infection, therefore requiring holistic care and expertise from various medical specialties. In fact, the clinical spectrum of presentation ranges from respiratory complaints to gastrointestinal, cardiac or neurological symptoms. In addition, COVID-19 pandemic has created a global burden of mental illness that affects the general population as well as healthcare practitioners. The aim of this manuscript is to provide a comprehensive and multidisciplinary insight into the complexity of this disease, reviewing current scientific evidence on COVID-19 management and treatment across several medical specialties involved in the in-hospital care of these patients.

An Italian Guidance Model for the Management of Suspected or Confirmed COVID-19 Patients in the Primary Care Setting

An outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 started in China's Hubei province at the end of 2019 has rapidly become a pandemic. In Italy, a great number of patients was managed in primary care setting and the role of general practitioners and physicians working in the first-aid emergency medical service has become of utmost importance to coordinate the network between the territory and hospitals during the pandemic. Aim of this manuscript is to provide a guidance model for the management of suspected, probable, or confirmed cases of SARS-CoV-2 infection in the primary care setting, from diagnosis to treatment, applying also the recommendations of the Italian Society of General Medicine. Moreover, this multidisciplinary contribution would analyze and synthetize the preventive measures to limit the spread of SARS-CoV-2 infection in the general population as well as the perspective for vaccines.

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.