Trends in oncological imaging during the COVID-19 pandemic through the vaccination era.

BACKGROUND: This study examines the impact that the COVID-19 pandemic has had on computed tomography (CT)-based oncologic imaging utilization. METHODS: We retrospectively analyzed cancer-related CT scans during four time periods: pre-COVID (1/5/20-3/14/20), COVID peak (3/15/20-5/2/20), post-COVID peak (5/3/20-12/19/20), and vaccination period (12/20/20-10/30/21). We analyzed CTs by imaging indication, setting, and hospital type. Using percentage decrease computation and Student's t-test, we calculated the change in mean number of weekly cancer-related CTs for all periods compared to the baseline pre-COVID period. This study was performed at a single academic medical center and three affiliated hospitals. RESULTS: During the COVID peak, mean CTs decreased (-43.0%, p < 0.001), with CTs for (1) cancer screening, (2) initial workup, (3) cancer follow-up, and (4) scheduled surveillance of previously treated cancer dropping by 81.8%, 56.3%, 31.7%, and 45.8%, respectively (p < 0.001). During the post-COVID peak period, cancer screenings and initial workup CTs did not return to prepandemic imaging volumes (-11.4%, p = 0.028; -20.9%, p = 0.024). The ED saw increases in weekly CTs compared to prepandemic levels (+31.9%, p = 0.008), driven by increases in cancer follow-up CTs (+56.3%, p < 0.001). In the vaccination period, cancer screening CTs did not recover to baseline (-13.5%, p = 0.002) and initial cancer workup CTs doubled (+100.0%, p < 0.001). The ED experienced increased cancer-related CTs (+75.9%, p < 0.001), driven by cancer follow-up CTs (+143.2%, p < 0.001) and initial workups (+46.9%, p = 0.007). CONCLUSIONS AND RELEVANCE: The pandemic continues to impact cancer care. We observed significant declines in cancer screening CTs through the end of 2021. Concurrently, we observed a 2× increase in initial cancer workup CTs and a 2.4× increase in cancer follow-up CTs in the ED during the vaccination period, suggesting a boom of new cancers and more cancer examinations associated with emergency level acute care.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI.

INTRODUCTION: Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. METHODS: Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). RESULTS: The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. CONCLUSION: The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study.

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

Long-term neuroimaging follow-up of COVID-19-related leukoencephalopathy.

More than a year after the start of the COVID-19 pandemic, long-term neurological manifestations of COVID-19 are increasingly being reported. The long-term sequelae of COVID-19-related leukoencephalopathy, however, remain unclear. Here, we present long-term neuroimaging follow-up in two cases of COVID-19-related leukoencephalopathy. The two cases demonstrate the utility of brain MRI for evaluating neurologic symptoms in critically ill patients with COVID-19, for diagnosis of underlying neural injury and prognostication of future recovery. The presence of leukoencephalopathy may result in chronic neurologic manifestations and may represent a poor prognosticator of neurologic recovery. The presence of leukoencephalomalacia on follow-up neuroimaging is potentially an indicator of irreversible white matter damage, which may be associated with more severe chronic deficits.

Trends in cancer imaging by indication, care setting, and hospital type during the COVID-19 pandemic and recovery at four hospitals in Massachusetts.

BACKGROUND: We aimed to investigate the effects of COVID-19 on computed tomography (CT) imaging of cancer. METHODS: Cancer-related CTs performed at one academic hospital and three affiliated community hospitals in Massachusetts were retrospectively analyzed. Three periods of 2020 were considered as follows: pre-COVID-19 (1/5/20-3/14/20), COVID-19 peak (3/15/20-5/2/20), and post-COVID-19 peak (5/3/20-11/14/20). 15 March 2020 was the day a state of emergency was declared in MA; 3 May 2020 was the day our hospitals resumed to non-urgent imaging. The volumes were assessed by (1) Imaging indication: cancer screening, initial workup, active cancer, and surveillance; (2) Care setting: outpatient and inpatient, ED; (3) Hospital type: quaternary academic center (QAC), university-affiliated community hospital (UACH), and sole community hospitals (SCHs). RESULTS: During the COVID-19 peak, a significant drop in CT volumes was observed (-42.2%, p < 0.0001), with cancer screening, initial workup, active cancer, and cancer surveillance declining by 81.7%, 54.8%, 30.7%, and 44.7%, respectively (p < 0.0001). In the post-COVID-19 peak period, cancer screening and initial workup CTs did not recover (-11.7%, p = 0.037; -20.0%, p = 0.031), especially in the outpatient setting. CT volumes for active cancer recovered, but inconsistently across hospital types: the QAC experienced a 9.4% decline (p = 0.022) and the UACH a 41.5% increase (p < 0.001). Outpatient CTs recovered after the COVID-19 peak, but with a shift in utilization away from the QAC (-8.7%, p = 0.020) toward the UACH (+13.3%, p = 0.013). Inpatient and ED-based oncologic CTs increased post-peak (+20.0%, p = 0.004 and +33.2%, p = 0.009, respectively). CONCLUSIONS: Cancer imaging was severely impacted during the COVID-19 pandemic. CTs for cancer screening and initial workup did not recover to pre-COVID-19 levels well into 2020, a finding that suggests more patients with advanced cancers may present in the future. A redistribution of imaging utilization away from the QAC and outpatient settings, toward the community hospitals and inpatient setting/ED was observed.

Pulmonary Vascular Manifestations of COVID-19 Pneumonia.

PURPOSE: To investigate pulmonary vascular abnormalities at CT pulmonary angiography (CT-PE) in patients with coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS: In this retrospective study, 48 patients with reverse-transcription polymerase chain reaction-confirmed COVID-19 infection who had undergone CT-PE between March 23 and April 6, 2020, in a large urban health care system were included. Patient demographics and clinical data were collected through the electronic medical record system. Twenty-five patients underwent dual-energy CT (DECT) as part of the standard CT-PE protocol at a subset of the hospitals. Two thoracic radiologists independently assessed all studies. Disagreement in assessment was resolved by consensus discussion with a third thoracic radiologist. RESULTS: Of the 48 patients, 45 patients required admission, with 18 admitted to the intensive care unit, and 13 requiring intubation. Seven patients (15%) were found to have pulmonary emboli. Dilated vessels were seen in 41 cases (85%), with 38 (78%) and 27 (55%) cases demonstrating vessel enlargement within and outside of lung opacities, respectively. Dilated distal vessels extending to the pleura and fissures were seen in 40 cases (82%) and 30 cases (61%), respectively. At DECT, mosaic perfusion pattern was observed in 24 cases (96%), regional hyperemia overlapping with areas of pulmonary opacities or immediately surrounding the opacities were seen in 13 cases (52%), opacities associated with corresponding oligemia were seen in 24 cases (96%), and hyperemic halo was seen in 9 cases (36%). CONCLUSION: Pulmonary vascular abnormalities such as vessel enlargement and regional mosaic perfusion patterns are common in COVID-19 pneumonia. Perfusion abnormalities are also frequently observed at DECT in COVID-19 pneumonia and may suggest an underlying vascular process.Supplemental material is available for this article.© RSNA, 2020.

Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19.

Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19. Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis. Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14-2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33-0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34-10.92) compared to patients without cerebrovascular disease. Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.

Right Ventricular Strain Is Common in Intubated COVID-19 Patients and Does Not Reflect Severity of Respiratory Illness.

BACKGROUND: Right ventricular (RV) dysfunction is common and associated with worse outcomes in patients with coronavirus disease 2019 (COVID-19). In non-COVID-19 acute respiratory distress syndrome, RV dysfunction develops due to pulmonary hypoxic vasoconstriction, inflammation, and alveolar overdistension or atelectasis. Although similar pathogenic mechanisms may induce RV dysfunction in COVID-19, other COVID-19-specific pathology, such as pulmonary endothelialitis, thrombosis, or myocarditis, may also affect RV function. We quantified RV dysfunction by echocardiographic strain analysis and investigated its correlation with disease severity, ventilatory parameters, biomarkers, and imaging findings in critically ill COVID-19 patients. METHODS: We determined RV free wall longitudinal strain (FWLS) in 32 patients receiving mechanical ventilation for COVID-19-associated respiratory failure. Demographics, comorbid conditions, ventilatory parameters, medications, and laboratory findings were extracted from the medical record. Chest imaging was assessed to determine the severity of lung disease and the presence of pulmonary embolism. RESULTS: Abnormal FWLS was present in 66% of mechanically ventilated COVID-19 patients and was associated with higher lung compliance (39.6 vs 29.4 mL/cmH2O, P = 0.016), lower airway plateau pressures (21 vs 24 cmH2O, P = 0.043), lower tidal volume ventilation (5.74 vs 6.17 cc/kg, P = 0.031), and reduced left ventricular function. FWLS correlated negatively with age (r = -0.414, P = 0.018) and with serum troponin (r = 0.402, P = 0.034). Patients with abnormal RV strain did not exhibit decreased oxygenation or increased disease severity based on inflammatory markers, vasopressor requirements, or chest imaging findings. CONCLUSIONS: RV dysfunction is common among critically ill COVID-19 patients and is not related to abnormal lung mechanics or ventilatory pressures. Instead, patients with abnormal FWLS had more favorable lung compliance. RV dysfunction may be secondary to diffuse intravascular micro- and macro-thrombosis or direct myocardial damage. TRIAL REGISTRATION: National Institutes of Health #NCT04306393. Registered 10 March 2020, https://clinicaltrials.gov/ct2/show/NCT04306393.

Implementation of the Radiological Society of North America Expert Consensus Guidelines on Reporting Chest CT Findings Related to COVID-19: A Multireader Performance Study.

BACKGROUND: RSNA expert consensus guidelines provide a framework for reporting CT findings related to COVID-19, but have had limited multireader validation. PURPOSE: To assess the performance of the RSNA guidelines and quantify interobserver variability in application of the guidelines in patients undergoing chest CT for suspected COVID-19 pneumonia. MATERIALS AND METHODS: A retrospective search from 1/15/20 to 3/30/20 identified 89 consecutive CT scans whose radiological report mentioned COVID-19. One positive or two negative RT-PCR tests for COVID-19 were considered the gold standard for diagnosis. Each chest CT scan was evaluated using RSNA guidelines by 9 readers (6 fellowship trained thoracic radiologists and 3 radiology resident trainees). Clinical information was obtained from the electronic medical record. RESULTS: There was strong concordance of findings between radiology training levels with agreement ranging from 60 to 86% among attendings and trainees (kappa 0.43 to 0.86). Sensitivity and specificity of "typical" CT findings for COVID-19 per the RSNA guidelines were on average 86% (range 72%-94%) and 80.2% (range 75-93%), respectively. Combined "typical" and "indeterminate" findings had a sensitivity of 97.5% (range 94-100%) and specificity of 54.7% (range 37-62%). A total of 163 disagreements were seen out of 801 observations (79.6% total agreement). Uncertainty in classification primarily derived from difficulty in ascertaining peripheral distribution, multiple dominant disease processes, or minimal disease. CONCLUSION: The "typical appearance" category for COVID-19 CT reporting has an average sensitivity of 86% and specificity rate of 80%. There is reasonable interreader agreement and good reproducibility across various levels of experience.

COVID-19: Recovery Models for Radiology Departments.

The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline.

Racial and Ethnic Disparities in Disease Severity on Admission Chest Radiographs among Patients Admitted with Confirmed Coronavirus Disease 2019: A Retrospective Cohort Study.

Background Disease severity on chest radiographs has been associated with higher risk of disease progression and adverse outcomes from coronavirus disease 2019 (COVID-19). Few studies have evaluated COVID-19-related racial and/or ethnic disparities in radiology. Purpose To evaluate whether non-White minority patients hospitalized with confirmed COVID-19 infection presented with increased severity on admission chest radiographs compared with White or non-Hispanic patients. Materials and Methods This single-institution retrospective cohort study was approved by the institutional review board. Patients hospitalized with confirmed COVID-19 infection between March 17, 2020, and April 10, 2020, were identified by using the electronic medical record (n = 326; mean age, 59 years ±17 [standard deviation]; male-to-female ratio: 188:138). The primary outcome was the severity of lung disease on admission chest radiographs, measured by using the modified Radiographic Assessment of Lung Edema (mRALE) score. The secondary outcome was a composite adverse clinical outcome of intubation, intensive care unit admission, or death. The primary exposure was the racial and/or ethnic category: White or non-Hispanic versus non-White (ie, Hispanic, Black, Asian, or other). Multivariable linear regression analyses were performed to evaluate the association between mRALE scores and race and/or ethnicity. Results Non-White patients had significantly higher mRALE scores (median score, 6.1; 95% confidence interval [CI]: 5.4, 6.7) compared with White or non-Hispanic patients (median score, 4.2; 95% CI: 3.6, 4.9) (unadjusted average difference, 1.8; 95% CI: 0.9, 2.8; P < .01). For both White (adjusted hazard ratio, 1.3; 95% CI: 1.2, 1.4; P < .001) and non-White (adjusted hazard ratio, 1.2; 95% CI: 1.1, 1.3; P < .001) patients, increasing mRALE scores were associated with a higher likelihood of experiencing composite adverse outcome with no evidence of interaction (P = .16). Multivariable linear regression analyses demonstrated that non-White patients presented with higher mRALE scores at admission chest radiography compared with White or non-Hispanic patients (adjusted average difference, 1.6; 95% CI: 0.5, 2.7; P < .01). Adjustment for hypothesized mediators revealed that the association between race and/or ethnicity and mRALE scores was mediated by limited English proficiency (P < .01). Conclusion Non-White patients hospitalized with coronavirus disease 2019 infection were more likely to have a higher severity of disease on admission chest radiographs than White or non-Hispanic patients, and increased severity was associated with worse outcomes for all patients. © RSNA, 2020 Online supplemental material is available for this article.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

PURPOSE: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. MATERIALS AND METHODS: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. RESULTS: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. CONCLUSIONS: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks.

PURPOSE: To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. MATERIALS AND METHODS: A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated. RESULTS: PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)). CONCLUSION: A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.

Imaging Volume Trends and Recovery During the COVID-19 Pandemic: A Comparative Analysis Between a Large Urban Academic Hospital and Its Affiliated Imaging Centers.

RATIONALE AND OBJECTIVES: While affiliated imaging centers play an important role in healthcare systems, little is known of how their operations are impacted by the COVID-19 pandemic. Our goal was to investigate imaging volume trends during the pandemic at our large academic hospital compared to the affiliated imaging centers. MATERIALS AND METHODS: This was a descriptive retrospective study of imaging volume from an academic hospital (main hospital campus) and its affiliated imaging centers from January 1 through May 21, 2020. Imaging volume assessment was separated into prestate of emergency (SOE) period (before SOE in Massachusetts on March 10, 2020), "post-SOE" period (time after "nonessential" services closure on March 24, 2020), and "transition" period (between pre-SOE and post-SOE). RESULTS: Imaging volume began to decrease on March 11, 2020, after hospital policy to delay nonessential studies. The average weekly imaging volume during the post-SOE period declined by 54% at the main hospital campus and 64% at the affiliated imaging centers. The rate of imaging volume recovery was slower for affiliated imaging centers (slope = 6.95 for weekdays) compared to main hospital campus (slope = 7.18 for weekdays). CT, radiography, and ultrasound exhibited the lowest volume loss, with weekly volume decrease of 41%, 49%, and 53%, respectively, at the main hospital campus, and 43%, 61%, and 60%, respectively, at affiliated imaging centers. Mammography had the greatest volume loss of 92% at both the main hospital campus and affiliated imaging centers. CONCLUSION: Affiliated imaging center volume decreased to a greater degree than the main hospital campus and showed a slower rate of recovery. Furthermore, the trend in imaging volume and recovery were temporally related to public health announcements and COVID-19 cases.

Lung apical findings in coronavirus disease (COVID-19) infection on neck and cervical spine CT.

PURPOSE: To evaluate the prevalence and features of lung apical findings on neck and cervical spine CTs performed in patients with COVID-19. METHODS: This was a retrospective, IRB-approved study performed at a large academic hospital in the USA. Between March 3, 2020, and May 6, 2020, 641 patients with COVID-19 infection diagnosed by RT-PCR received medical care at our institution. A small cohort of patients with COVID-19 infection underwent neck or cervical spine CT imaging for indications including stroke, trauma, and neck pain. The lung apices included in the field of view on these CT scans were reviewed for the presence of findings suspicious for COVID-19 pneumonia, including ground-glass opacities, consolidation, or crazy-paving pattern. The type and frequency of these findings were recorded and correlated with clinical information including age, gender, and symptoms. RESULTS: Thirty-four patients had neck or spine CTs performed before or concurrently with a chest CT. Of this group, 17 (50%) had unknown COVID-19 status at the time of neck or spine imaging and 10 (59%) of their CT studies had findings in the lung apices consistent with COVID-19 pneumonia. CONCLUSION: Lung apical findings on cervical spine or neck CTs consistent with COVID-19 infection are common and may be encountered on neuroimaging performed for non-respiratory indications. For these patients, the emergency radiologist may be the first physician to suspect underlying COVID-19 infection.

Detection of Unsuspected Coronavirus Disease 2019 Cases by Computed Tomography and Retrospective Implementation of the Radiological Society of North America/Society of Thoracic Radiology/American College of Radiology Consensus Guidelines.

PURPOSE: The purpose of this article was to report the utility of computed tomography (CT) for detecting unsuspected cases of Coronavirus disease 2019 (COVID-19) and the utility of the Radiological Society of North America (RSNA)/Society of Thoracic Radiology (STR)/American College of Radiology (ACR) consensus guidelines for COVID-19 reporting. MATERIALS AND METHODS: A total of 22 patients of the 156 reverse transcriptase polymerase chain reaction confirmed COVID-19 patients who were hospitalized between March 27, 2020 and March 31, 2020 at our quaternary care academic medical center and who underwent CT imaging within 1 week of admission were included in this retrospective study. Demographics and clinical data were extracted from the electronic medical record system. Two thoracic radiologists independently categorized each CT study on the basis of RSNA/STR/ACR consensus guidelines. Disagreement in categorization was resolved by consensus discussion with a third thoracic radiologist. RESULTS: At the time of imaging, 16 patients (73%) were suspected of COVID-19, and 6 patients (27%) were not. Common symptoms at presentation were fever (73%), cough (77%), and gastrointestinal symptoms (59%). An overall 63% of suspected COVID-19 patients exhibited shortness of breath, whereas 0 unsuspected COVID-19 patients did (P=0.02). On the basis of the RSNA consensus guidelines, 68%, 18%, 9%, and 5% of studies were categorized as "typical appearance," "indeterminate appearance," "atypical appearance," and "negative for pneumonia," respectively. There was no difference of category distribution between suspected and unsuspected COVID-19 patients (P=0.20), with "typical appearance" being the most prevalent in both (69% vs. 67%, respectively). CONCLUSIONS: It is important to recognize imaging features of COVID-19 pneumonia even in unsuspected patients. Implementation of the RSNA/STR/ACR consensus guidelines may increase consistency of reporting and convey the level of suspicion for COVID-19 to other health care providers, with "typical appearance" especially warranting further attention.