2021 Top Images in Radiology: Radiology In Training Editors' Choices.

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Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Multisystem Assessment of the Imaging Manifestations of Coagulopathy in Hospitalized Patients With Coronavirus Disease (COVID-19).

BACKGROUND. Coronavirus disease (COVID-19) is known to be associated with a distinct form of coagulopathy. OBJECTIVE. The purpose of this study was to describe the imaging manifestations of COVID-19-associated coagulopathy across anatomic sites and modalities in hospitalized patients and to identify clinical variables associated with positive imaging findings. METHODS. We conducted a retrospective review of consecutive adult patients with COVID-19 admitted to our hospital over a 3-week period. Data on patient demographics, hematologic values, cross-sectional imaging examinations, and clinical outcomes (death and intubation) were collected. Imaging was reviewed for manifestations of coagulopathy. Multivariable logistic regression analyses were performed to assess associations of patient demographics, hematologic markers, and outcomes with the need for imaging and imaging manifestations of coagulopathy. RESULTS. Of 308 hospitalized patients with COVID-19, 142 (46%) underwent 332 cross-sectional imaging examinations. Of these, 37 (26%) had imaging results positive for coagulopathy. The most common imaging manifestations of coagulopathy were pulmonary embolus (n = 21) on contrast-enhanced CT or CTA, thrombus in the upper- or lower-extremity veins (n = 13) on Doppler ultrasound, end-organ infarction in the bowel (n = 4) and kidney (n = 4) on contrast-enhanced CT, and thrombus or parenchymal infarction in the brain (n = 2) on contrast-enhanced CTA or MRI with MRA. Among patients with imaging results positive for coagulopathy, eight (22%) had multisite involvement. Thrombi were multifocal in four of five patients with positive upper-extremity and three of eight patients with positive lower-extremity examination results and involved superficial veins, deep veins, or both. In multivariable analysis, intubation (p < .001) and prolonged prothrombin time (p = .04) were significantly associated with undergoing imaging. No patient variable was significantly associated with imaging results positive for coagulopathy (p > .05). CONCLUSION. Imaging commonly shows manifestations of coagulopathy in hospitalized patients with COVID-19. Over one-fifth of patients with such manifestations show multisite involvement. Clinical variables poorly predict which patients have positive imaging results, indicating a complementary role of imaging in detecting COVID-19-associated coagulopathy. CLINICAL IMPACT. In patients with COVID-19 with suspected systemic coagulopathy, pulmonary CTA, extremity Doppler ultrasound, contrast-enhanced abdominal CT, and contrast-enhanced brain MRI and MRA may all be appropriate in the absence of imaging contraindications.

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.

Abdominal Imaging Findings in COVID-19: Preliminary Observations.

Background Angiotensin-converting enzyme 2, a target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), demonstrates its highest surface expression in the lung, small bowel, and vasculature, suggesting abdominal viscera may be susceptible to injury. Purpose To report abdominal imaging findings in patients with coronavirus disease 2019. Materials and Methods In this retrospective cross-sectional study, patients consecutively admitted to a single quaternary care center from March 27 to April 10, 2020, who tested positive for SARS-CoV-2 were included. Abdominal imaging studies performed in these patients were reviewed, and salient findings were recorded. Medical records were reviewed for clinical data. Univariable analysis and logistic regression were performed. Results A total of 412 patients (average age, 57 years; range, 18 to >90 years; 241 men, 171 women) were evaluated. A total of 224 abdominal imaging studies were performed (radiography, n = 137; US, n = 44; CT, n = 42; MRI, n = 1) in 134 patients (33%). Abdominal imaging was associated with age (odds ratio [OR], 1.03 per year of increase; P = .001) and intensive care unit (ICU) admission (OR, 17.3; P < .001). Bowel-wall abnormalities were seen on 31% of CT images (13 of 42) and were associated with ICU admission (OR, 15.5; P = .01). Bowel findings included pneumatosis or portal venous gas, seen on 20% of CT images obtained in patients in the ICU (four of 20). Surgical correlation (n = 4) revealed unusual yellow discoloration of the bowel (n = 3) and bowel infarction (n = 2). Pathologic findings revealed ischemic enteritis with patchy necrosis and fibrin thrombi in arterioles (n = 2). Right upper quadrant US examinations were mostly performed because of liver laboratory findings (87%, 32 of 37), and 54% (20 of 37) revealed a dilated sludge-filled gallbladder, suggestive of bile stasis. Patients with a cholecystostomy tube placed (n = 4) had negative bacterial cultures. Conclusion Bowel abnormalities and gallbladder bile stasis were common findings on abdominal images of patients with coronavirus disease 2019. Patients who underwent laparotomy often had ischemia, possibly due to small-vessel thrombosis. © RSNA, 2020.