Calcified amorphous tumors (CATs) of the heart are rare non-neoplastic cardiac masses primarily found in the mitral valve or annulus. However, their exact pathogenesis remains unknown. In this case report, we describe the CT and MRI findings and differentiating features of cardiac a CAT in the left atrium of a 79-year-old female.

Calcified amorphous tumors (CATs) of the heart are rare non-neoplastic cardiac masses primarily found in the mitral valve or annulus. However, their exact pathogenesis remains unknown. In this case report, we describe the CT and MRI findings and differentiating features of cardiac a CAT in the left atrium of a 79-year-old female.

Calcified amorphous tumors (CATs) of the heart are rare non-neoplastic cardiac masses primarily found in the mitral valve or annulus. However, their exact pathogenesis remains unknown. In this case report, we describe the CT and MRI findings and differentiating features of cardiac a CAT in the left atrium of a 79-year-old female.

Background: The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients. Methods: From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1–2 but is indicated for groups 3–4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups. Results: Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3–4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1–4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3–4 than in groups 1–2 (group 1–4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events. Conclusion The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

Acute trauma is a common cause of mortality in individuals aged < 40 years. As organ preservation has become important in treating trauma patients, the treatment is shifting from surgical management to non-operative management. A multidisciplinary team approach, including interventional radiology (IR), is essential for the optimal management of trauma patients, as IR plays an important role in injury evaluation and management. IR also contributes significantly to achieving the best clinical outcomes in critically ill trauma patients. This pictorial essay aims to present and summarize various interventional treatments in trauma patients requiring critical care

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

Acute trauma is a common cause of mortality in individuals aged < 40 years. As organ preservation has become important in treating trauma patients, the treatment is shifting from surgical management to non-operative management. A multidisciplinary team approach, including interventional radiology (IR), is essential for the optimal management of trauma patients, as IR plays an important role in injury evaluation and management. IR also contributes significantly to achieving the best clinical outcomes in critically ill trauma patients. This pictorial essay aims to present and summarize various interventional treatments in trauma patients requiring critical care

Purpose: The aim of this study was to evaluate the pancreatic fat fraction (PFF) using magnetic resonance imaging (MRI) in children with and without obesity and to correlate PFF with body mass index (BMI) z-score, hepatic fat fraction (HFF), and ultrasonography-derived pancreato-perihepatic fat index (PPHFI). Methods: This prospective study included 45 children with obesity and 19 without obesity (control group). PFF and HFF were quantitatively assessed using the abdominal multi-echo Dixon method for MRI. The PPHFI was assessed using transabdominal ultrasonography. Anthropometric, MRI, and ultrasonographic characteristics were compared between the two groups. Correlations between PFF, HFF, PPHFI, and BMI z-scores in each group were also analyzed. Results: The PFF, HFF, PPHFI, and BMI z-score were higher in the group with obesity than in the control group (PFF: 6.65±3.42 vs. 1.78±0.55, HFF: 19.5±13.0 vs. 2.31±1, PPHFI: 3.65 ±1.63 vs. 0.94±0.31, BMI z-score: 2.27±0.56 vs. 0.42±0.54, p<0.01, respectively). PFF was correlated with BMI z-scores, PPHFI, and HFF in the obesity group, and multivariate analysis showed that PFF was strongly correlated with BMI z-score and PPHFI (p<0.05). The BMI z-score was strongly correlated with PFF in the control group (p<0.01). Conclusion These results suggest that MRI-derived PFF measures are associated with childhood obesity. PFF and PPHFI were also highly correlated in the obesity group. Therefore, PFF may be an objective index of pancreatic fat content and has the potential for clinical utility as a non-invasive biomarker for the assessment of childhood obesity.

Body Mass Index ; Child ; Humans ; Insulin ; Non-alcoholic Fatty Liver Disease ; Obesity ; Overweight ; Pediatric Obesity ; Prevalence ; Waist Circumference