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Objective: Characterize the CT findings of abdominopelvic Castleman disease, including a new observation involving the perinodal fat. Methods: Multi-centre search at 5 institutions yielded 76 adults (mean age, 42.1 ± 14.3 years; 38 women/38 men) meeting inclusion criteria of histopathologically proven Castleman disease with nodal involvement at abdominopelvic CT. Retrospective review of the dominant nodal mass was assessed for size, attenuation, and presence of calcification, and for prominence and soft-tissue infiltration of the perinodal fat. Hypervascular nodal enhancement was based on both subjective and objective comparison with aortic blood pool attenuation. Results: Abdominal involvement was unicentric in 48.7% (37/76) and multicentric in 51.3% (39/76), including 31 cases with extra-abdominal involvement. Histopathologic subtypes included hyaline vascular variant (HVV), plasma cell variant (PCV), mixed HVV/PCV, and HHV-8 variant in 39, 25, 3 and 9 cases, respectively. The dominant nodal mass measured 4.4 ± 1.9 cm and 3.2 ± 1.7 cm in mean long- and short-axis, respectively, and appeared hypervascular in 58.6% (41/70 with IV contrast). Internal calcification was seen in 22.4% (17/76). Infiltration of the perinodal fat, with or without hypertrophy, was present in 56.6% (43/76), more frequent with hypervascular vs non-hypervascular nodal masses (80.5% vs 20.7%; P < .001). Among HVV cases, 76.9% were unicentric, 71.1% appeared hypervascular, and 69.2% demonstrated perinodal fat infiltration. Conclusion: Hypervascular nodal masses demonstrating prominence and infiltration of perinodal fat at CT can suggest the specific diagnosis of Castleman disease, especially the HVV. Advances in knowledge: Abdominopelvic nodal masses that demonstrate hypervascular enhancement and prominent infiltration of the perinodal fat at CT can suggest the diagnosis of Castleman disease, but nonetheless requires tissue sampling. [ABSTRACT FROM AUTHOR]

The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep learning-based methodologies that operate during the different steps of the image creation, prior to or after the traditional image formation process (eg, filtered backprojection [FBP] or iterative reconstruction [IR]), or alternatively by fully replacing FBP or IR techniques. DLR algorithms effectively facilitate the reduction of image noise associated with low photon counts from reduced radiation dose protocols. DLR methods have emerged as an effective solution to ameliorate limitations observed with prior CT image reconstruction algorithms, including FBP and IR algorithms, which are not able to preserve image texture and diagnostic performance at low radiation dose levels. An additional advantage of DLR algorithms is their high reconstruction speed, hence targeting the ideal triad of features for a CT image reconstruction (ie, the ability to consistently provide diagnostic-quality images and achieve radiation dose imaging levels as low as reasonably possible, with high reconstruction speed). An accumulated body of evidence supports the clinical use of DLR algorithms in abdominal imaging across multiple CT imaging tasks. The authors explore the technical aspects of DLR CT algorithms and examine various approaches to image synthesis in DLR creation. The clinical applications of DLR algorithms are highlighted across various abdominal CT imaging domains, with emphasis on the supporting evidence for diverse clinical tasks. An overview of the current limitations of and outlook for DLR algorithms for CT is provided. © RSNA, 2024.

In patients with cancer, spontaneous renal bleeding can stem from a range of underlying factors, necessitating precise diagnostic tools for effective patient management. Benign and malignant renal tumors are among the primary culprits, with angiomyolipomas and renal cell carcinomas being the most common among them. Vascular anomalies, infections, ureteral obstructions, and coagulation disorders can also contribute to renal-related bleeding. Cross-sectional imaging techniques, particularly ultrasound and computed tomography (CT), play pivotal roles in the initial detection of renal bleeding. Magnetic resonance imaging and CT are preferred for follow-up evaluations and aid in detecting underlying enhancing masses. IV contrast-enhanced ultrasound can provide additional information for active bleeding detection and differentiation. This review article explores specific disorders associated with or resembling spontaneous acute renal bleeding in patients with renal tumors; it focuses on the significance of advanced imaging techniques in accurately identifying and characterizing renal bleeding in these individuals. It also provides insights into the clinical presentations, imaging findings, and treatment options for various causes of renal bleeding, aiming to enhance the understanding, diagnosis, and management of the issue.

Objective: The purpose of this article is to summarize the epidemiologic characteristics, clinical aspects, and radiologic appearance of as well as the management considerations and differential diagnoses for noncutaneous primary melanomas occurring at specific anatomic sites. Primary ocular, sinonasal, meningeal, biliary, adrenal, alimentary tract, and genitourinary melanomas are highlighted., Conclusion: Noncutaneous primary melanomas are a complex group of malignancies with biologic findings that are unique when compared with findings for cutaneous melanomas. Each noncutaneous primary melanoma has its own specific diagnostic and management challenges, depending on the anatomic location where they arise.