Primary central nervous system lymphomas (PCNSLs) are typically intraparenchymal. A subset of PCNSLs predominantly arises in the ventricles, with minimal parenchymal involvement. We review the clinical, radiological, and pathological features of ventricle-predominant PCNSLs (VP-PCNSLs) in 40 previously reported cases and report 5 additional cases. Including all cases of VP-PCNSLs (n = 45), 38% were diffuse large B-cell lymphomas (DLBCL), 11% were Burkitt lymphomas, 7% were MALT lymphomas, 4% were T-cell lymphomas, and 40% were lymphomas, not otherwise classified. VP-PCNSLs show rapid clinical progression. Patients present at a median age of 60.5 years. Unique clinical and radiological features distinguish them from other intraventricular tumors, including advanced age, edema, multifocality, hyperdensity, early and avid post-contrast enhancement, restricted diffusion, and positron emission tomography (PET) hypermetabolism. Including our cases, which were all DLBCL, and all previously reported DLBCL cases (n = 10), 8 of 10 show germinal center B-cell-like (GCB) phenotype, contrasting the high prevalence of non-germinal center B-cell-like (non-GCB) phenotype of parenchymal DLBCL PCNSLs. MYD88 L265P mutation was detected in three of our five cases. Ventricle-predominant PCNSLs are clinically and radiologically distinct, and the DLBCLs may be pathologically distinct. Further recognition of this entity may help to evaluate the role of therapies, possibly including surgical resection.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10014-019-00354-x | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Oral Radiol
January 2025
Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul University, Istanbul, Turkey.
Objectives: This study evaluates the potential of pulp volume/total tooth-volume measurements of canine teeth in relation to chronologic age in patients with cleft lip and palate (CLP). The significance of this study lies in its exploration of the usability of these measurements for age determination in CLP patients, providing a novel perspective to the existing literature.
Methods: Cone beam computed tomography images of 33 patients (16 females, 17 males) with unilateral CLP aged 14-45 years and 33 age- and sex-matched healthy individuals (16 females, 17 males) were retrospectively evaluated.
J Cancer Educ
January 2025
Université de Reims Champagne-Ardenne, CRESTIC, Reims, France.
Cancer remains a leading cause of mortality worldwide, requiring physicians to understand multidisciplinary treatments. This study assessed the impact of a clinical rotation in a cancer center on medical students' knowledge of cancer treatments from a multidisciplinary perspective. A traditional single-department rotation was compared to a multidisciplinary rotation to determine whether broader exposure enhances knowledge and prepares students for multidisciplinary care.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!