• COVID-19
• MRI
• Relative Value Units (RVUs)
• clinical operations
• computed tomography (CT)
• coronavirus
• hospital
• imaging volumes
• pandemic
• radiology department

• Infection prevention and control
• Medical imaging
• Surveys

The coronavirus disease 2019 (COVID-19) pandemic altered education, exams, and residency applications for United States medical students.

To determine the specific impact of the pandemic on US medical students and its correlation to their anxiety levels.

An 81-question survey was distributed via email, Facebook and social media groups using REDCap^TM. 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 25^th 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.

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.

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.

• Medical student
• SARS-CoV-2
• Anxiety
• Stress
• Psychological
• Impact clinical

The use of ionising radiation results in occupational exposure to medical imaging professionals, requiring routine monitoring. This study aims to assess the effect of increased utilisation of mobile X‐ray units, mobile imaging of non‐routine body regions and radiographer work practice changes for impact on staff radiation dose during the early stages of the COVID‐19 pandemic.

A retrospective analysis of general radiology departments across two metropolitan hospitals was performed. Personal radiation monitor exposure reports between January 2019 and December 2020 were analysed. Statistical analysis was conducted using a Mann–Whitney U test when comparing each quarter, from 2019 to 2020. Categorical data were compared using a Chi‐squared test.

Mobile X‐ray use during the pandemic increased approximately 1.7‐fold, with the peak usage observed in September 2020. The mobile imaging rate per month of non‐routine body regions increased from approximately 6.0–7.8%. Reported doses marginally increased during Q2, Q3 and Q4 of 2020 (in comparison to 2019 data), though was not statistically significant (Q2: P = 0.13; Q3: P = 0.31 and Q4 P = 0.32). In Q1, doses marginally decreased and were not statistically significant (P = 0.22).

Increased utilisation and work practice changes had no significant effect on reported staff radiation dose. The average reported dose remained significantly lower than the occupational dose limits for radiation workers of 20 mSv.

• Clinical site
• general
• general radiography
• medical imaging
• radiation effects
• respiratory

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.

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.

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.

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.

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.

• COVID-19
• SARS-CoV-2
• acute coronary syndrome
• coronary artery disease
• nursing
• pathophysiology

• COVID-19
• Medical imaging service
• Optimization strategies
• Pandemic
• Radiography
• Radiology department

• COVID-19
• SARS-CoV-2
• SERM
• drug repositioning
• estrogen receptor
• raloxifene

• COVID-19
• SARS-CoV-2
• nosocomial transmission
• nuclear medicine
• patient safety
• positron emission tomography-computed tomography
• precautions
• reopening
• ventilation/perfusion scintigraphy

• COVID-19
• neuroradiology
• personal protective equipment
• radiology
• tele-radiology

•

2 pathways: negative, suspected/confirmed Covid-19.

• COVID-19
• Preparedness
• Radiology
• SARS-CoV-2

• Betacoronavirus
• COVID-19
• Coronavirus
• Coronavirus Infections/epidemiology
• Coronavirus Infections/prevention & control
• Coronavirus Infections/virology
• Health Care Rationing
• Health Services Accessibility
• Humans
• Infection Control
• Pandemics/prevention & control
• Patient Care
• Personal Protective Equipment
• Pneumonia, Viral/epidemiology
• Pneumonia, Viral/prevention & control
• Pneumonia, Viral/virology
• Policy
• Practice Guidelines as Topic
• Radiography
• Radiology
• SARS-CoV-2
• United States/epidemiology

• COVID-19
• Protective measures
• Questionnaire
• Radiographers
• Radiology department

Mass casualty incidents, by nature of their scale and unpredictability, can rapidly overwhelm health infrastructure. Preparation is the key to managing these crises with the lowest risk to emergency and health personnel, while providing maximal life saving measures. We present an overview of the multitiered planning that should go into forming a well set out emergency response plan and one that is capable of being adapted to a wide range of mass casualty scenarios. We highlight the ethical implications that a healthcare team faces while making challenging decisions rapidly in a high-pressure environment. Radiology trainees should be aware of the response systems in place at their institutions and the role that is expected of them in mass casualty incidents.

• Pandemics