Radiology
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Background Detection of cerebral lesions at MRI may benefit from a chemically stable and more sensitively detected gadolinium-based contrast agent (GBCA). Gadopiclenol, a macrocyclic GBCA with at least twofold higher relaxivity, is currently undergoing clinical trials in humans. Purpose To determine the relationship between MRI contrast enhancement and the injected dose of gadopiclenol in a glioma rat model compared with those of conventional GBCA at label dose. ⋯ Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Tweedle in this issue.
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Multicenter Study
Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.
Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. ⋯ For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.
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Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. ⋯ Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.