Jonasson Church (yakasia7)

The objective of this study was to determine how the social media impact of the radiological literature has changed during the Covid-19 pandemic. Altmetric Attention scores were collected for all articles in five leading radiology journals over a 5-year period ending in June 2020, and temporal smoothing yielded the filtered Altmetric Attention (fAA) score. Natural language processing methods were used to label articles with major topic areas. A forecasting model was used to identify periods of outlier behavior in the fAA score aggregated across all journals, for each journal individually, and stratified by article topic area. The distributions of fAA scores prior to the onset of the pandemic were statistically compared to those during the pandemic. For journals exhibiting increased fAA scores, the frequency distributions of articles not related to Covid-19 was compared to that prior to the pandemic. During the pandemic, we found sustained outliers and statistically significant increases in the aggregate fAA score across all five journals, as well as for Radiology, American Journal of Roentgenology, and Academic Radiology individually. Articles related to Covid-19, thoracic imaging, and radiology education also experienced significantly increased fAA scores during the pandemic period. We did not find significantly decreased rates of publication of non-Covid articles in the journals experiencing elevated fAA scores. Social media engagement with the radiological literature significantly increased during the Covid-19 pandemic. This preferentially affected certain journals and articles addressing specific topics, reflecting the intense public interest in the diagnosis and treatment of Covid-19. Social media engagement with the radiological literature significantly increased during the Covid-19 pandemic. This preferentially affected certain journals and articles addressing specific topics, reflecting the intense public interest in the diagnosis and treatment of Covid-19. Contrast-enhanced mammography (CEM) is a novel breast imaging technique that can provide additional information of breast tissue blood supply. This study aimed to test the possibility of CEM in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions with consideration of morphology and distribution. Data of patients with suspicious malignant calcification-only lesions (BI-RADS 4) on low-energy CEM and proved pathologic diagnoses were retrospectively collected. Two junior radiologists independently reviewed the two sets of CEM images, low-energy images (LE) to describe the calcifications by morphology and distribution type, and recombined images (CE) to record the presence of enhancement. Low-risk and high-risk groups were divided by calcification morphology, distribution, and both, respectively. Positive predictive values and misdiagnosis rates (MDR) were compared between LE-only reading and CE reading. Diagnostic performance was also tested using machine learning method. The study included 74 lesions (26 malignant and 48 benign). Positive predictive values were significantly higher and MDRs were significantly lower using CE images than using LE alone for both the low-risk morphology type and low-risk distribution type (P< .05). MDRs were significantly lower when using CE images (18.18%-24.00%) than using LE images alone in low-risk group (76.36%-80.00%) (P< .05). Using a machine learning method, significant improvements in the area under the receiver operating characteristic curve were observed in both low-risk and high-risk groups. CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more. CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular,