Diverse conditions comprise the spectrum of renal sinus pathologies, which have diagnostic and therapeutic implications for patients. Using CT imaging as a lens, this exhaustive review examines the representation of these pathologies. The article begins with a concise synopsis of renal anatomy and the specialized CT methodologies utilized to achieve excellent visualization. Transformational cell carcinoma, leiomyosarcoma, renal cell carcinoma, multilocular nephroma, and lymphoma are among the tumoral origins of the renal sinus pathologies that are investigated. Further, vascular pathologies including fistulas, hematomas, and aneurysms are included in the discourse, along with parapelvic and peripelvic cysts, and lipomatosis. In addition to urolithiasis and encrusted uretero-pyelitis, the review examines the consequences of metal toxicity and non-neoplastic conditions. With a focus on critical CT imaging findings that aid in the provision of an accurate diagnosis, every pathology is meticulously examined. With the intention of improving clinical decision-making and patient care, this article intends to function as a valuable resource for radiologists, clinicians, and researchers who are engaged in the interpretation and comprehension of renal sinus pathologies.
In the original publication [...].
(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm's ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit's Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model's clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology.
