European radiology
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Review Meta Analysis
Use of Vesical Imaging-Reporting and Data System (VI-RADS) for detecting the muscle invasion of bladder cancer: a diagnostic meta-analysis.
To comprehensively assess the diagnostic performance of Vesical Imaging-Reporting and Data System (VI-RADS) score for detecting the muscle invasion of bladder cancer. ⋯ • VI-RADS score has high sensitivity and specificity for predicting muscle invasion. • The diagnostic efficiencies of VI-RADS 3 and VI-RADS 4 as the cutoff value are similar. • VI-RADS score could be used for detecting muscle invasion of bladder cancer in clinical practice.
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To characterize the chest computed tomography (CT) findings of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) according to clinical severity. We compared the CT features of common cases and severe cases, symptomatic patients and asymptomatic patients, and febrile and afebrile patients. ⋯ • The clinical features and predominant patterns of abnormalities on CT for asymptomatic, typic common, and severe cases were summarized. These findings may help clinicians to identify severe patients quickly at admission. • Clinicians should be cautious that CT findings of afebrile/asymptomatic patients are not better than the findings of other types of patients. These patients should also be quarantined. • The use of chest CT as the main screening method in epidemic areas is recommended.
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To explore the relationship between the imaging manifestations and clinical classification of COVID-19. ⋯ • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.
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Comparative Study
Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods. ⋯ • Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions. • Radiomics model based on multiparametric MRI has better performance than single-sequence models. • The machine learning methods RFE with SVM perform best in the current cohort.