Hydronephrosis and pyonephrosis

Urinary tract infections (UTIs) are a frequent clinical problem in infants and children, and they may develop into serious complications with long-term sequelae. Their prevalence varies according to age and sex: males are more interested in the neonatal period and in the first year of age, while females are more involved after that time. The clinical outcome and the severity of UTIs depend on many risk factors including urinary tract malformations and dysfunctions, virulence and properties of the pathogen, host response to the infection, and promptness of diagnosis and management

Structured reporting of computed tomography in the polytrauma patient assessment. A Delphi consensus proposal

Objectives: To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology. Methods: A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach’s alpha (Cα) correlation coefficient. Results: The final SR form included 118 items (6 in the “Patient Clinical Data” section, 4 in the “Clinical Evaluation” section, 9 in the “Imaging Protocol” section, and 99 in the “Report” section). The experts’ overall mean score and sum of scores were 4.77 (range 1–5) and 257.56 (range 206–270) in the first Delphi round, and 4.96 (range 4–5) and 208.44 (range 200–210) in the second round, respectively. In the second Delphi round, the experts’ overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87). Conclusions: Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team

Neck emergency due to parathyroid adenoma bleeding: a case report

<p>Abstract</p> <p>Introduction</p> <p>The spontaneous rupture of a parathyroid adenoma accompanied by extracapsular hemorrhage is a rare, potentially fatal, condition and is a cervicomediastinal surgical emergency.</p> <p>Case presentation</p> <p>This report describes an atypical two-step spontaneous rupture of an asymptomatic parathyroid adenoma in a 56-year-old Caucasian woman who presented with a painful mass in the right side of her neck.</p> <p>Conclusion</p> <p>Based on this case report and similar cases reported in the medical literature, a diagnosis of extracapsular parathyroid hemorrhage should be considered when a non-traumatic sudden neck swelling coexists with hypercalcemia and regional ecchymosis.</p

Role of artificial intelligence in oncologic emergencies: a narrative review

Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described