A Global Review of the Impacts of the Coronavirus (COVID-19) Pandemic on Radiology Practice, Finances, and Operations.

The Coronavirus Disease 2019 (COVID-19) pandemic ushered in rapid changes in healthcare, including radiology, globally. This review discusses the impact of the pandemic on various radiology departments globally. We analyze the implications of the COVID-19 pandemic on the imaging volumes, finances, and clinical operations of radiology departments in 2020. Studies from health systems and outpatient imaging centers were analyzed, and the activity throughout 2020 was compared to the pre-pandemic activity, including activity during similar timeframes in 2019. Imaging volumes across modalities, including MRI and CT scans, were compared, as were the Relative Value Units (RVUs) for imaging finances. Furthermore, we compared clinical operations, including staffing and sanitation procedures. We found that imaging volumes in private practices and academic centers decreased globally. The decreases in volume could be attributed to delayed patient screenings, as well as the implementation of protocols, such as the deep cleaning of equipment between patients. Revenues from imaging also decreased globally, with many institutions noting a substantial decline in RVUs and revenue compared with pre-COVID-19 levels. Our analysis thus found significant changes in the volumes, finances, and operations of radiology departments due to the COVID-19 pandemic.

SARS-CoV-2 Infection, Inflammation, Immunonutrition, and Pathogenesis of COVID-19.

The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A bibliometrics analysis on author's keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is "self-destroy and rebuild", which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms' clearance could be only a passive bystander response to this destroy-rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a few possible coping strategies to mitigate COVID-19, including vaccination, are discussed.

Black Fungus and beyond: COVID-19 associated infections.

Globally, many hospitalized COVID-19 patients can experience an unexpected acute change in status, prompting rapid and expert clinical assessment. Superimposed infections can be a significant cause of clinical and radiologic deviations in this patient population, further worsening clinical outcome and muddling the differential diagnosis. As thrombotic, inflammatory, and medication-induced complications can also trigger an acute change in COVID-19 patient status, imaging early and often plays a vital role in distinguishing the cause of patient decline and monitoring patient outcome. While the common radiologic findings of COVID-19 infection are now widely reported, little is known about the clinical manifestations and imaging findings of superimposed infection. By discussing case studies of patients who developed bacterial, fungal, parasitic, and viral co-infections and identifying the most frequently reported imaging findings of superimposed infections, physicians will be more familiar with common infectious presentations and initiate a directed workup sooner. Ultimately, any abrupt changes in the expected COVID-19 imaging presentation, such as the presence of new consolidations or cavitation, should prompt further workup to exclude superimposed opportunistic infection.

Educational, psychosocial, and clinical impact of SARS-CoV-2 (COVID-19) pandemic on medical students in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic altered education, exams, and residency applications for United States medical students. AIM: To determine the specific impact of the pandemic on US medical students and its correlation to their anxiety levels. METHODS: An 81-question survey was distributed via email, Facebook and social media groups using REDCapTM. To investigate risk factors associated with elevated anxiety level, we dichotomized the 1-10 anxiety score into low (≤ 5) and high (≥ 6). This cut point represents the 25th percentile. There were 90 (29%) shown as low anxiety and 219 (71%) as high anxiety. For descriptive analyses, we used contingency tables by anxiety categories for categorical measurements with chi square test, or mean ± STD for continuous measurements followed by t-test or Wilcoxson rank sum test depending on data normality. Least Absolute Shrinkage and Selection Operator was used to select important predictors for the final multivariate model. Hierarchical Poisson regression model was used to fit the final multivariate model by considering the nested data structure of students clustered within State. RESULTS: 397 medical students from 29 states were analyzed. Approximately half of respondents reported feeling depressed since the pandemic onset. 62% of participants rated 7 or higher out of 10 when asked about anxiety levels. Stressors correlated with higher anxiety scores included "concern about being unable to complete exams or rotations if contracting COVID-19" (RR 1.34; 95%CI: 1.05-1.72, P = 0.02) and the use of mental health services such as a "psychiatrist" (RR 1.18; 95%CI: 1.01-1.3, P = 0.04). However, those students living in cities that limited restaurant operations to exclusively takeout or delivery as the only measure of implementing social distancing (RR 0.64; 95%CI: 0.49-0.82, P < 0.01) and those who selected "does not apply" for financial assistance available if needed (RR 0.83; 95%CI: 0.66-0.98, P = 0.03) were less likely to have a high anxiety. CONCLUSION: COVID-19 significantly impacted medical students in numerous ways. Medical student education and clinical readiness were reduced, and anxiety levels increased. It is vital that medical students receive support as they become physicians. Further research should be conducted on training medical students in telemedicine to better prepare students in the future for pandemic planning and virtual healthcare.

The role of imaging techniques in understanding and evaluating the long-term pulmonary effects of COVID-19.

INTRODUCTION: Limited data exist regarding the long-term pulmonary sequelae of COVID-19. Identifying features utilizing multiple imaging modalities engenders a clearer picture of the illness's long-term consequences. AREAS COVERED: This review encompasses the common pulmonary findings associated with different imaging modalities during acute and late remission stages of COVID-19 pneumonia. EXPERT OPINION: Chest x-ray, a common preliminary diagnostic imaging technique, is not optimal for extended care due to limited tissue contrast resolution providing suboptimal assessment of pulmonary pathology and subtle interval changes. Ultrasound may be utilized on a case-by-case basis in certain patient populations, or in countries with limited resources. Chest CT's accessibility, high tissue contrast and spatial resolution make it the foremost modality for long-term COVID-19 follow-up. While MRI can viably monitor extrapulmonary disease due to its lack of radiation and high inherent soft-tissue contrast, it has limited pulmonary utility due to motion artifact and alveolar gas decreasing lung signal. Although 18F-FDG-PET/CT is costly and has limited specificity, it can provide molecular level data and inflammation quantification. Lung perfusion scintigraphy may also explain COVID-19 induced thromboembolic events and persistent dyspnea despite normal structural imaging and testing results. Correlating the long-term pulmonary findings of COVID-19 with each imaging modality is essential in elucidating the post-recovery course.

COVID-19 pandemic revisited: lessons the radiology community has learned a year later.

For more than 1 year, COVID-19 pandemic has impacted every aspect of our lives. This paper reviews the major challenges that the radiology community faced over the past year and the impact the pandemic had on the radiology practice, radiologist-in-training education, and radiology research. The lessons learned from COVID-19 pandemic can help the radiology community to be prepared for future outbreaks and new pandemics, preserve good habits, enhance cancer screening programs, and adapt to the changes in radiology education and scientific meetings.

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. METHODS: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. RESULTS: Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. CONCLUSIONS: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. ADVANCES IN KNOWLEDGE: We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

A review of neuroradiological abnormalities in patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to various neurological manifestations. There is an urgent need for a summary of neuroimaging findings to accelerate diagnosis and treatment plans. We reviewed prospective and retrospective studies to classify neurological abnormalities observed in patients with the SARS-CoV-2 infection. METHODS: The relevant studies published in Scopus, PubMed and Clarivate Analytics databases were analysed. The search was performed for full-text articles published from 23 January 2020 to 23 February 2021. RESULTS: In 23 studies the number of patients with SARS-CoV-2 infection was 20,850 and the number of patients with neurological manifestations was 1996 (9.5%). The total number of patients with neuroradiological abnormalities was 602 (2.8%). SARS-CoV-2 has led to various neuroimaging abnormalities which can be categorised by neuroanatomical localisation of lesions and their main probable underlying pathogenesis. Cranial nerve and spinal root abnormalities were cranial neuritis and polyradiculitis. Parenchymal abnormalities fell into four groups of: (a) thrombosis disorders, namely ischaemic stroke and sinus venous thrombosis; (b) endothelial dysfunction and damage disorders manifested as various types of intracranial haemorrhage and posterior reversible encephalopathy syndrome; (c) hypoxia/hypoperfusion disorders of leukoencephalopathy and watershed infarction; and (d) inflammatory disorders encompassing demyelinating disorders, encephalitis, vasculitis-like disorders, vasculopathy and cytotoxic lesions of the corpus callosum. Leptomeninges disorders included meningitis. Ischaemic stroke was the most frequent abnormality in these studies. CONCLUSION: The review study suggests that an anatomical approach to the classification of heterogeneous neuroimaging findings in patients with SARS-CoV-2 and neurological manifestations would lend itself well for use by practitioners in diagnosis and treatment planning.

Addressing ethnic disparities in imaging utilization and clinical outcomes for COVID-19.

PURPOSE: Racial and ethnic disparities have exacerbated during the COVID-19 pandemic as the healthcare system is overwhelmed. While Hispanics are disproportionately affected by COVID-19, little is known about ethnic disparities in the hospital settings. This study investigates imaging utilization and clinical outcomes between Hispanic and non-Hispanic COVID-19 patients in the Emergency Department (ED) and during hospitalization. METHODS: Through retrospective chart review, we included 331 symptomatic COVID-19 patients (mean age 53.2 years) at a metropolitan healthcare system from March to June 2020. Poisson regression was used to compare diagnostic imaging utilization and clinical outcomes between Hispanic and non-Hispanic patients. RESULTS: After adjusting for confounders, no statistically significant difference was found between Hispanic and non-Hispanic patients for the number of weekly chest X-rays. Results were categorized into four clinical outcomes: ED management (0.16 ± 0.05 vs. 0.14 ± 0.8, p:0.79); requiring inpatient management (1.31 ± 0.11 vs. 1.46 ± 0.16, p:0.43); ICU admission without invasive ventilation (1.4 ± 0.17 vs. 1.35 ± 0.26, p:0.86); and ICU admission and ventilator support (3.29 ± 0.22 vs. 3.59 ± 0.37, p:0.38). There were no statistically significant relative differences in adjusted prevalence rate between ethnic groups for all clinical outcomes (p > 0.05). There was a statistically significant longer adjusted length of stay (days) in non-Hispanics for two subcohorts: inpatient management (8.16 ± 0.31 vs. 9.72 ± 0.5, p < 0.01) and ICU admission without invasive ventilation (10.39 ± 0.57 vs. 13.45 ± 1.13, p < 0.01). CONCLUSIONS: For Hispanic and non-Hispanic COVID-19 patients in the ED or hospitalized, there were no statistically significant differences in imaging utilization and clinical outcomes.

FDG-PET/CT of COVID-19 and Other Lung Infections.

While not conventionally used as the first-line modality, [18F]-2-fluoro-2-deoxy-D-glucose (FDG) - positron emission tomography/computed tomography (PET/CT) can identify infection and inflammation both earlier and with higher sensitivity than anatomic imaging modalities [including chest X-ray (CXR), computed tomography (CT), and magnetic resonance imaging (MRI)]. The extent of inflammation and, conversely, recovery within the lungs, can be roughly quantified on FDG-PET/CT using maximum standardized uptake value (SUVmax) values. The Coronavirus disease 2019 (COVID-19) pandemic has highlighted the value of FDG-PET/CT in diagnosis, elucidation of acute pulmonary and extrapulmonary manifestations, and long-term follow up. Similarly, many other pulmonary infections such as previously documented coronaviruses, aspergillosis, blastomycosis, candidiasis, coccidioidomycosis, cryptococcosis, histoplasmosis, mucormycosis, and typical/atypical mycobacterial infections have all been identified and characterized using FDG-PET/CT imaging. The goal of this review is to summarize the actual and potential benefits of FDG-PET/CT in the imaging of COVID-19 and other lung infections. Further research is necessary to determine the best indications and clinical applications of FDG-PET/CT, improve its specificity, and ultimately ascertain how this modality can best be utilized in the diagnostic work up of infectious pathologies.

Reopening the country: Recommendations for nuclear medicine departments.

The best practices for nuclear medicine departments to operate safely during the COVID-19 pandemic have been debated in the literature recently. However, as many governments have started to ease restrictions in activity due to COVID-19, a set of guidelines is needed to resume routine patient care throughout the world. The nonessential or elective procedures which were previously postponed or canceled during the COVID-19 pandemic will gradually restart in the following weeks despite the continued risks. In this paper, we aim to review some of the most effective general precautions to restart the regular nuclear medicine operations safely.

SARS-CoV-2 reinfection: "New baseline" imaging concept in the era of COVID-19.

Recent reports have suggested COVID-19 relapse or reinfection may lead to readmission, which may cause a diagnostic challenge between recently infected patients and reinfections. Compounding this problem is the post-viral lung sequela that may be expected after COVID-19 pneumonia, similar to both SARS and MERS. Although chest imaging may play a role in the diagnosis of primary SARS-CoV-2 infection, reinfection or relapse of COVID-19 will have similar imaging findings. A "new-baseline" imaging can be obtained from COVID-19 patients at the time of hospital discharge or clinical recovery. This new reference can not only determine if readmissions are from relapse or reinfection of COVID-19, resolving COVID-19 or potentially a different viral infection (influenza), but also for long term sequela of COVID-19 lung infection. Strategic use of imaging before discharge may be helpful in the subset of the population at the highest risk of a secondary viral infection such as influenza. Determining the residual abnormalities in post-discharge imaging can guide us in the long-term management of patients for many years to come.

Machine learning based predictors for COVID-19 disease severity.

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with [Formula: see text] for predicting ICU need and [Formula: see text] for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.

COVID-19 vaccine is here: practical considerations for clinical imaging applications.

Imaging tools are potentially able to provide valuable data regarding the development of an efficient vaccine against viral diseases. Tracking immune cells in vivo by imaging modalities can help us understand the intrinsic behaviors of immune cells in response to vaccine components. Imaging patterns at the vaccination site and draining lymph nodes might provide useful information about the vaccine potency. Besides, serial lung CT imaging has been purposed to evaluate vaccine efficiency regarding its protection against typical lung lesions of viral pneumonias. On the other hand, vaccination causes various confusing radiologic patterns that pose diagnostic challenges for clinicians and pitfalls for reading radiologists. This manuscript reviews potential applications of imaging modalities in the process of vaccine development and also goes over some of the imaging findings/pitfalls following vaccination.

COVID-19 or non-COVID viral pneumonia: How to differentiate based on the radiologic findings?

Influenza viruses were responsible for most adult viral pneumonia. Presently, coronavirus disease 2019 (COVID-19) has evolved into serious global pandemic. COVID-19 outbreak is expected to persist in months to come that will be synchronous with the influenza season. The management, prognosis, and protection for these two viral pneumonias differ considerably and differentiating between them has a high impact on the patient outcome. Reverse transcriptase polymerase chain reaction is highly specific but has suboptimal sensitivity. Chest computed tomography (CT) has a high sensitivity for detection of pulmonary disease manifestations and can play a key-role in diagnosing COVID-19. We reviewed 47 studies and delineated CT findings of COVID-19 and influenza pneumonia. The differences observed in the chest CT scan can be helpful in differentiation. For instance, ground glass opacities (GGOs), as the most frequent imaging finding in both diseases, can differ in the pattern of distribution. Peripheral and posterior distribution, multilobular distribution, pure or clear margin GGOs were more commonly reported in COVID-19, whereas central or peri-bronchovascular GGOs and pure consolidations were more seen in influenza A (H1N1). In review of other imaging findings, further differences were noticed. Subpleural curvilinear lines, sugar melted sign, intra-lesional vascular enlargement, reverse halo sign, and fibrotic bands were more reported in COVID-19 than H1N1, while air space nodule, tree-in-bud, bronchiectasia, pleural effusion, and cavitation were more seen in H1N1. This delineation, when combined with clinical manifestations and laboratory results may help to differentiate these two viral infections.

COVID-19 in pregnancy: a systematic review of chest CT findings and associated clinical features in 427 patients.

PURPOSE: Our purpose was to conduct a comprehensive systematic review of all existing literature regarding imaging findings on chest CT and associated clinical features in pregnant patients diagnosed with COVID-19. MATERIALS & METHODS: A literature search was conducted on April 21, 2020 and updated on July 24, 2020 using PubMed, Embase, World Health Organization, and Google Scholar databases. Only studies which described chest CT findings of COVID-19 in pregnant patients were included for analysis. RESULTS: A total of 67 articles and 427 pregnant patients diagnosed with COVID-19 were analyzed. The most frequently encountered pulmonary findings on chest CT were ground-glass opacities (77.2%, 250/324), posterior lung involvement (72.5%, 50/69), multilobar involvement (71.8%, 239/333), bilateral lung involvement (69.4%, 231/333), peripheral distribution (68.1%, 98/144), and consolidation (40.9%, 94/230). Pregnant patients were also found to present more frequently with consolidation (40.9% vs. 21.0-31.8%) and pleural effusion (30.0% vs. 5.0%) in comparison to the general population. Associated clinical features included antepartum fever (198 cases), lymphopenia (128 cases), and neutrophilia (97 cases). Of the 251 neonates delivered, 96.8% had negative RT-PCR and/or IgG antibody testing for COVID-19. In the eight cases (3.2%) of reported neonatal infection, tests were either conducted on samples collected up to 72 h after birth or were found negative on all subsequent RT-PCR tests. CONCLUSION: Pregnant patients appear to present more commonly with more advanced COVID-19 CT findings compared to the general adult population. Furthermore, characteristic laboratory abnormalities found in pregnant patients tended to mirror those found in the general patient population. Lastly, results from neonatal testing suggest a low risk of vertical transmission.