European radiology
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To evaluate the prevalence of acute pulmonary embolism (APE) in non-hospitalized COVID-19 patients referred to CT pulmonary angiography (CTPA) by the emergency department. ⋯ • Acute pulmonary embolism was found in 18% of non-hospitalized COVID-19 patients referred by the emergency department to CTPA. Two (15%) patients had main, four (30%) lobar, and seven (55%) segmental acute pulmonary embolism. • Five of 13 (38%) patients with acute pulmonary embolism had a moderate clinical type. • Severity and radiological features of COVID-19 pneumonia showed no significant difference between patients with or without acute pulmonary embolism.
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To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. ⋯ • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.
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To determine the consistency between CT findings and real-time reverse transcription-polymerase chain reaction (RT-PCR) and to investigate the relationship between CT features and clinical prognosis in COVID-19. ⋯ • Chest CT is valuable for the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. • The early CT findings of COVID-19 in ICU patients differed from those of discharged patients.
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(1) To identify the factors predicting arterial extravasation in pelvic trauma and (2) to assess the efficacy of preperitoneal pelvic packing (PPP) in controlling arterial hemorrhage. ⋯ • Unstable Young-Burgess pelvic fractures are predictors of arterial hemorrhage in pelvic trauma. • Pelvic angiography and embolization should precede PPP wherever feasible.
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To determine imaging hallmarks for distinguishing intrahepatic mass-forming biliary carcinomas (IMBCs) from hepatocellular carcinoma (HCC) and to validate their diagnostic ability using Bayesian statistics. ⋯ • Combined interpretation of CT and MRI to identify hallmark features was useful in discriminating intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma. • Bayesian method-based post-test probability combining all hallmark features determined in study 1 showed high (> 90%) sensitivity and specificity for distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma. • If the post-test probability or the confidence was ≥ 80% when combining the imaging features of CT and MRI, the high specificity of > 95% was achieved without any loss of sensitivity to distinguish hepatocellular carcinoma from intrahepatic mass-forming biliary carcinomas.