Emergency Radiology最新文献

Epiploic appendagitis of the vermiform appendix is a rare cause of right lower abdominal pain that can mimic acute appendicitis and result in unnecessary surgery. Despite this, the condition can be managed with non-steroidal anti-inflammatory drugs alone. Due to the lack of characteristic physical or laboratory findings, accurate diagnosis by imaging is crucial. The aim of this case report is to emphasize this uncommon condition to prevent misdiagnosis and avoid unnecessary surgical interventions. A 57-year-old man presented with a 2-day history of abdominal pain and tenderness in the right abdominal region. Laboratory results were within the normal range. The surgeon diagnosed him as distal appendicitis or colonic diverticulitis and treated him with antibiotics, leading to improvement within several days. A subsequent review of the plain computed tomography images by the radiologist detected an oval fat density surrounded by a high-intensity rim and a high-density spot in the center at the tip of normal vermiform appendix. This led to a diagnosis of epiploic appendagitis on the vermiform appendix. Epiploic appendagitis is characterized by inflammation and ischemia resulting from torsion of the epiploic appendage. It can occur not only on the colon but also on the appendix. The imaging findings in this case were typical of epiploic appendagitis on the appendix. It is imperative for clinicians to be familiar with the clinical presentation and imaging findings of epiploic appendagitis on the appendix to ensure an accurate diagnosis, reduce unnecessary surgeries, thereby enhancing patient outcomes.

Purpose: To assess whether adult trauma center status influences radiology resident performance on trauma cases in the Emergent/Critical Care Imaging SIMulation (WIDI SIM) exam.

Materials and methods: This retrospective study analyzed 29,290 WIDI SIM exam scores from 110 adult trauma cases across 55 radiology residency programs. Residents were categorized by training level-R1 (n = 17,801), R2 (n = 9,136), R3 (n = 1,826), R4 (n = 527)-and by their program's adult trauma center designation: Level 1 (n = 20,121), Level 2 (n = 1,870), Level 3 (n = 1,029), Level 4 (n = 487), and no trauma designation (n = 5,834). A Generalized Linear Mixed Model with a negative binomial distribution was used to evaluate the effect of trauma center status on resident performance, adjusting for resident level, imaging modality, and case specialty.

Results: After adjusting for confounding variables, there was no statistically significant difference in resident scores based on adult trauma center status (p > 0.05 for all trauma levels compared to no trauma designation). Resident level significantly influenced performance, with higher-level residents scoring better than R1 residents (p < 0.001 for R2-R4). Imaging modality and case specialty also significantly affected scores. Residents performed better on MR, US, and XR modalities compared to CT (p ≤ 0.002), and scored lower on chest, cardiovascular, musculoskeletal, and neuro cases compared to abdominopelvic cases (p < 0.001).

Conclusion: Adult trauma center status did not significantly impact radiology resident performance on trauma cases in the WIDI SIM exam. Resident training level, imaging modality, and case specialty were significant factors influencing performance. These findings suggest that resident education and exposure to diverse imaging modalities and specialties are more critical determinants of diagnostic accuracy than the trauma center designation of their training program.

Purpose: Splenic artery embolization (SAE) has emerged as a promising alternative for managing variceal bleeding secondary to portal hypertension (PH). This study aims to elucidate the significance of SAE in managing esophageal variceal bleeding in patients with PH, providing an overview of its efficacy, safety, and role in PH management.

Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA standards. EMBASE, PubMed, Scopus, and Web of Science databases were searched from inception until April 14, 2024. Original observational and clinical studies on SAE in managing variceal bleeding due to PH were included. Meta-analyses were performed using a random-effects model, and publication bias was assessed using regression and rank correlation tests for funnel plot asymmetry.

Results: Eighteen studies met the inclusion criteria, encompassing 531 patients. The meta-analysis revealed a significant reduction in variceal bleeding post-SAE (RD = -0.86; 95% CI: -0.97, -0.75; p < 0.001). Complete resolution of varices was observed in 26% of patients (95% CI: 11%, 45%; p = 0.006), and 78% showed improvement in variceal grade (95% CI: 43%, 88%; p < 0.001). SAE significantly increased platelet counts (SMD = 1.15; 95% CI: 0.63, 1.68; p < 0.001). Common complications included post-embolization syndrome, and the overall complication rate was low.

Conclusions: This systematic review and meta-analysis study supports the efficacy and safety of SAE in managing variceal bleeding due to PH, demonstrating significant reductions in bleeding, improvements in variceal grade, and increases in platelet counts.

Purpose: Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD in US images, which may help the diagnosis of AD by novice radiologists or non-professionals.

Methods: There were 374 US images from patients treated before June 30, 2022. The images were classified as AD-positive and AD-negative images. Among them, 90% of images were used as the training set, and 10% of images were used as the test set. A Densenet-169 model and a VGG-16 model were used in this study and compared with two human readers.

Results: DL models demonstrated high sensitivity and AUC for diagnosing abdominal AD in US images, and DL models showed generally better performance than human readers.

Conclusion: Our findings demonstrated the efficacy of DL-aided diagnosis of abdominal AD in US images, which can be helpful in emergency settings.

Upper tract urothelial carcinoma (UTUC) is a rare and challenging subset of the more frequently encountered urothelial carcinomas (UCs), comprising roughly 5-7% of all UCs and less than 10% of all renal tumors. Hematuria is a common presenting symptom in the emergency setting, often prompting imaging to rule out serious etiologies, with UTUC especially posing as a diagnostic challenge. These UTUC lesions of the kidney and ureter are often small, mimicking other pathologies, and are more aggressive than typical UC of the bladder, emphasizing the importance of timely and accurate diagnosis. Multidetector computed tomography urography (CTU) is the standard imaging modality for diagnosis, tumor staging, and surgical planning. Various postprocessing techniques like multiplanar reconstructions, maximal intensity projection (MIP) images, and 3D volumetric rendering technique (VRT) are crucial for accurate detection. In addition, 3D cinematic rendering (CR) is a novel technique that employs advanced illumination models, producing images with realistic shadows and increased surface detail, outperforming traditional VRT. We will review the distinctive imaging features between UTUC and infiltrating mimicking lesions on CTU in patients who presented with hematuria, in conjunction with advanced postprocessing techniques, ultimately improving diagnostic confidence and preoperative planning in the emergency context.

Introduction: Computed tomography (CT) angiography is commonly utilized to quickly identify vascular injuries caused by blunt cervical trauma. It is often conducted alongside a cervical spine CT, based on established criteria. This study assessed the prevalence of cervical vascular injuries identified via CT angiography (CTA) in patients who had negative findings on cervical CT scans.

Materials and methods: A retrospective study was performed on patients who experienced blunt trauma from January 2020 to December 2022 and underwent both cervical CT and CTA. The sample size was determined using the formula: n = (Z^2 * P * (1 - P)) / E^2, assuming a 99% confidence interval, a 2% margin of error, and a proportion of 0.05.

Results: A total of 1,165 patients presented with acute blunt trauma to the head and neck during the study period. Out of those, 800 patients (68.7%) had negative cervical CT scans and only 5 patients (0.6%) were found to have vascular injuries on CTA, with an average age of 44.2 years. Regarding the severity of the injuries, three were classified as grade I and two as grade II. On the other hand, of the 365 patients with positive cervical CT, 44 patients (12%) had vascular injury on CTA, including 16 patients (4.5%) with grades III and IV injuries.

Conclusion: The findings of this study suggest that CTA in patients with negative cervical CT scans seldom reveals vascular injuries, with no injuries exceeding grade II. This highlights the selective utility of CTA in this patient group.

Purpose: To evaluate radiology residents' ability to accurately identify three specific types of orthopedic trauma using radiographic imaging within a simulated on-call environment.

Methods: We utilized the Wisdom in Diagnostic Imaging Emergent/Critical Care Radiology Simulation (WIDI SIM) to assess residents' preparedness for independent radiology call. The simulation included 65 cases, with three focusing on orthopedic trauma: sacral ala, femoral neck, and pediatric tibial/Toddler's fractures. Faculty graded residents' responses using a standardized 10-point rubric and categorized errors as observational (failing to identify key findings) or interpretive (incorrect conclusions despite correct identification of findings).

Results: 321 residents evaluated sacral ala fracture radiographs and received an average score of 1.29/10, with 8.71 points lost to observational errors. Only 6% produced effective reports (scores ≥ 7), while 80% made critical errors (scores < 2). For femoral neck fracture CT images (n = 316 residents), the average score was 2.48/10, with 6.71 points lost to observational errors. 25% produced effective reports, and 66% made critical errors. Pediatric tibial/Toddler's fracture radiographs (n = 197 residents) yielded an average score of 2.94/10, with 6.60 points lost to observational errors. 29% generated effective reports, while 71% made critical errors.

Conclusion: Radiology residents demonstrated significant difficulty in identifying these orthopedic trauma cases, with errors primarily attributed to observational deficiencies. These findings suggest a need for targeted educational interventions in radiology residency programs to improve the identification of these fractures.

Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.

Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.

Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024. In phase 1, a steering committee (7 panelists) established key themes- curation; validity; human factors; workflow; barriers; future avenues; and ethics- and generated an edited, collated long-list of statements. In phase 2, two Delphi rounds using anonymous RAND/UCLA Likert grading were conducted with web-based data capture (round 1) and a bespoke excel document with literature hyperlinks (round 2). Between rounds, editing and knowledge synthesis helped maximize consensus. Statements reaching ≥80% agreement were included in the final document.

Results: Delphi rounds 1 and 2 consisted of 81 and 78 items, respectively.18/21 expert panelists (86%) responded to round 1, and 15 to round 2 (17% drop-out). Consensus was reached for 65 statements. Observations were summarized and contextualized. Statements with unanimous consensus centered around transparent methodologic reporting; testing for generalizability and robustness with external data; and benchmarking performance with appropriate metrics and baselines. A manuscript draft was circulated to panelists for editing and final approval.

Conclusions: The document is meant as a framework to foster best-practices and further discussion among researchers working on various aspects of emergency and trauma radiology AI.