European radiology
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To investigate the feasibility of a deep learning-based detection (DLD) system for multiclass lesions on chest radiograph, in comparison with observers. ⋯ • The DLD system was feasible for detection with pattern classification of multiclass lesions on chest radiograph. • The DLD system had high performance of image-wise classification as normal or abnormal chest radiographs (AUROC, 0.985) and showed especially high specificity (99.0%). • In lesion-wise detection of multiclass lesions, the DLD system outperformed all 9 observers (FOM, 0.962 vs. 0.886; p < 0.001).
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To update the national diagnostic reference levels (DRLs) for adult CT in Switzerland using dose management software and to compare them to the previous Swiss DRLs from 2010. ⋯ • Dose management software allows the establishment of DRLs based on big data. • Updated Swiss DRLs for adult CT are substantially lower compared with those from 2010. • Swiss DRLs are low compared with other national DRLs.
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To examine whether the texture analysis of dual-time-point (DTP) F-18-fluorodeoxyglucose (18F-FDG)-PET/CT imaging can differentiate between 18F-FDG-avid benign and malignant pulmonary lesions. ⋯ • Malignant pulmonary lesions showed significantly higher SUV-related (SUVmax and SUVmean) and volumetric (MTV and TLG) parameters than benign pulmonary lesions in both early and delayed images. • Malignant pulmonary lesions showed significantly more heterogeneous18F-FDG uptake than benign pulmonary lesions in both early and delayed images. • Combined use of independent parameters extracted from DTP imaging might yield a high diagnostic accuracy to differentiate between benign and malignant18F-FDG-avid pulmonary lesions.