European radiology
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Multicenter Study Comparative Study
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.
To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. ⋯ • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.
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Multicenter Study
Lung base CT findings in COVID-19 adult patients presenting with acute abdominal complaints: case series from a major New York City health system.
To describe demographic, clinical, and lung base CT findings in COVID-19 patients presenting with abdominal complaints. ⋯ • COVID-19 infected patients can present with acute abdominal symptoms, especially in non-elderly patients with underlying health conditions, and may frequently require hospitalisation (81%). • There was no difference in lung base CT findings between patients who were discharged and those who were hospitalised. • Lung base CT findings included multifocal and peripheral ground-glass opacities, consistent with published reports.
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To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). ⋯ • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.
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Multicenter Study
COVID-19 impact assessment on the French radiological centers: a nationwide survey.
To determine the impact of the COVID-19 on the CT activities in French radiological centers during the epidemic peak. ⋯ • Over the 4-week survey period, 117,686 chest CT (CTtotal) were performed among the responding centers, including 61,784 (52%) CT performed for COVID-19 (CTcovid). • Across the country, the ratio CTcovid/CTtotal varied from 0.36 to 0.59 and depended significantly on the local epidemic density (p = 0.003). • In clinical practice, in a context of growing epidemic, in France, chest CT was used as a surrogate to RT-PCR for patient triage.
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Sonoelastography has been increasingly used to investigate musculoskeletal disorders. The aim of this meta-analysis was to investigate the utility of sonoelastography in diagnosing rotator cuff tendon pathology and pertinent disorders. ⋯ • Supraspinatus and infraspinatus tendons are likely to have decreased elasticity in shoulders with adhesive capsulitis, as assessed by shear wave sonoelastography. • There was no significant difference in tendon elasticity between shoulders with and without rotator cuff tendinopathy or tendon tears when evaluated by strain and shear wave sonoelastography.