Radiology
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Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using chest CT for COVID-19, results of chest CT and reverse transcription polymerase chain reaction (RT-PCR) assays were compared together and with the final discharge diagnosis used as the reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2, 2020, and April 24, 2020. ⋯ Sensitivity, specificity, negative predictive value, and positive predictive value of chest CT in the diagnosis of COVID-19 were 2319 of 2564 (90%; 95% CI: 89, 91), 2056 of 2260 (91%; 95% CI: 91, 92), 2056 of 2300 (89%; 95% CI: 87, 90), and 2319 of 2524 (92%; 95% CI: 91, 93), respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of patients suspected of having coronavirus disease 2019 was successful. © RSNA, 2021 Online supplemental material is available for this article.
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Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. ⋯ The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.
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Clinical Trial
Diagnostic Performance of Chest CT for SARS-CoV-2 Infection in Individuals with or without COVID-19 Symptoms.
Background The use of chest CT for coronavirus disease 2019 (COVID-19) diagnosis or triage in health care settings with limited severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) capacity is controversial. COVID-19 Reporting and Data System (CO-RADS) categorization of the level of COVID-19 suspicion might improve diagnostic performance. Purpose To investigate the value of chest CT with CO-RADS classification to screen for asymptomatic SARS-CoV-2 infections and to determine its diagnostic performance in individuals with COVID-19 symptoms during the exponential phase of viral spread. ⋯ Conclusion CT with Coronavirus Disease 2019 Reporting and Data System (CO-RADS) had good diagnostic performance in symptomatic individuals, supporting its application for triage. Sensitivity in asymptomatic individuals was insufficient to justify its use as a first-line screening approach. Incidental detection of CO-RADS 3 or greater in asymptomatic individuals should trigger testing for respiratory pathogens. © RSNA, 2020 Online supplemental material is available for this article.
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Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. ⋯ At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.
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Background Intimate partner violence (IPV) is a global social and public health problem, but published literature regarding the exacerbation of physical IPV during the coronavirus disease 2019 (COVID-19) pandemic is lacking. Purpose To assess the incidence, patterns, and severity of injuries in IPV victims during the COVID-19 pandemic in 2020 compared with the prior 3 years. Materials and Methods The demographics, clinical presentation, injuries, and radiologic findings of patients reporting physical abuse arising from IPV during the statewide COVID-19 pandemic between March 11 and May 3, 2020, were compared with data from the same period for the past 3 years. ⋯ Patients who experienced IPV during the COVID-19 pandemic were more likely to be White; 17 (65%) victims in 2020 were White compared with 11 (26%) in the prior years (P = .007). Conclusion There was a higher incidence and severity of physical intimate partner violence (IPV) during the coronavirus disease 2019 (COVID-19) pandemic compared with the prior 3 years. These results suggest that victims of IPV delayed reaching out to health care services until the late stages of the abuse cycle during the COVID-19 pandemic. © RSNA, 2020.