Radiology
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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. ⋯ 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.
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Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. ⋯ On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.
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Background Atypical manifestations of coronavirus disease 2019 (COVID-19) are being encountered as the pandemic unfolds, leading to non-chest CT scans that may uncover unsuspected pulmonary disease. Purpose To investigate patients with primary nonrespiratory symptoms who underwent CT of the abdomen or pelvis or CT of the cervical spine or neck with unsuspected findings highly suspicious for pulmonary COVID-19. Materials and Methods This retrospective study from March 10, 2020, to April 6, 2020, involved three institutions, two in a region considered a hot spot (area of high prevalence) for COVID-19. ⋯ Major interventions (vasopressor medication or intubation) were required for 29 of 119 (24%) patients, and 27 of 119 (23%) died. Patients who underwent CT of the cervical spine or neck had worse outcomes than those who underwent abdominal or pelvic CT (P = .01). Conclusion In a substantial percentage of patients with primary nonrespiratory symptoms who underwent non-chest CT, CT provided evidence of coronavirus disease 2019-related pneumonia. © RSNA, 2020.
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Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. ⋯ With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.