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Objective In the era of SARS-CoV-2, the risk of infectious airborne aerosol generation during otolaryngologic procedures has been an area of increasing concern. The objective of this investigation was to quantify airborne aerosol production under clinical and surgical conditions and examine efficacy of mask mitigation strategies. Study design Prospective quantification of airborne aerosol generation during surgical and clinical simulation. Setting Cadaver laboratory and clinical examination room. Subjects and methods Airborne aerosol quantification with an optical particle sizer was performed in real time during cadaveric simulated endoscopic surgical conditions, including hand instrumentation, microdebrider use, high-speed drilling, and cautery. Aerosol sampling was additionally performed in simulated clinical and diagnostic settings. All clinical and surgical procedures were evaluated for propensity for significant airborne aerosol generation. Results Hand instrumentation and microdebridement did not producdard surgical masks did not.Background Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these assng the ribosome. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Cho in this issue.Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodulesy nodules. © RSNA, 2020 Online supplemental material is available for this article.Background Dual-energy CT allows virtual noncontrast (VNC) attenuation and iodine density measurements from contrast material-enhanced examination, potentially enabling adrenal lesion characterization. However, data regarding diagnostic performance remain limited, and combined diagnostic values have never been investigated. Purpose To determine whether VNC attenuation, iodine density, and combination of the two allow reliable differentiation between adrenal adenomas and metastases. Clamidine Materials and Methods This retrospective study included patients with adrenal lesions who underwent unenhanced and portal venous phase dual-energy CT between January 2017 and December 2018. Unenhanced, contrast-enhanced, and VNC attenuation, as well as iodine density, were measured for each lesion. Agreement between unenhanced and VNC attenuation was assessed by using Wilcoxon rank-sum test, Pearson correlation coefficient, and Bland-Altman plot. The ratio of iodine density to VNC attenuation was calculated for lesions with positiv, 0.54; 95% CI 0.45, 0.62). Iodine density yielded moderate performance (sensitivity of 78% [80 of 102] [95% CI 69%, 86%] and specificity of 71% [40 of 56] [95% CI 58%, 83%], with a threshold of ≥1.82 mg/mL). The iodine-to-VNC ratio was higher in adenomas than in metastases (mean, 14.5 vs 4.6; P less then .001), with sensitivity of 95% (97 of 102; 95% CI 89%, 98%) and specificity of 95% (53 of 56; 95% CI 85%, 99%), with a threshold of 6.7 or greater. Conclusion Contrast-enhanced dual-energy CT during the portal venous phase enabled accurate differentiation between adrenal adenomas and metastases by combining virtual noncontrast attenuation and iodine density. Virtual noncontrast imaging alone led to overestimates of adenoma attenuation, and iodine density alone had limited discriminatory utility. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Hindman and Megibow in this issue.Background Gadoxetic acid is classified by the American College of Radiology as a group III gadolinium-based contrast agent (GBCA), which indicates that there are limited data regarding nephrogenic systemic fibrosis (NSF) risk, but there are few if any unconfounded cases of NSF. Purpose To perform a systematic review and meta-analysis of gadoxetic acid adverse events, including immediate hypersensitivity reactions, NSF, and intracranial gadolinium retention. Materials and Methods Original research studies, case series, and case reports that reported adverse events in patients undergoing gadoxetic acid-enhanced MRI were searched in MEDLINE (1946-2019), Embase (1947-2019), CENTRAL (March 2019), and Scopus (1946-2019). The study protocol was registered at Prospero (number 162811). Risk of bias was evaluated by using Quality Assessment of Diagnostic Accuracy Studies-2, or QUADAS-2. Meta-analysis of proportions was performed by using random-effects modeling. Upper bound of 95% confidence interval (CI) for risk of NSF was determined.