Chondrogenic tumors are typically well recognized on radiographs, but differentiation between benign and malignant cartilaginous lesions can be difficult both for the radiologist and for the pathologist. Diagnosis is based on a combination of clinical, radiological and histological findings. While treatment of benign lesions does not require surgery, the only curative treatment for chondrosarcoma is resection. This article (1) emphasizes the update of the WHO classification and its diagnostic and clinical effects; (2) describes the imaging features of the various types of cartilaginous tumors, highlighting findings that can help differentiate benign from malignant lesions; (3) presents differential diagnoses; and (4) provides pathologic correlation. We attempt to offer valuable clues in the approach to this vast entity.
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http://dx.doi.org/10.1067/j.cpradiol.2023.01.005 | 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.
J 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
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has been the subject of research for many years in the context of improving the effectiveness of imaging methods for the diagnosis of brain tumor, stroke, and Alzheimer's disease, among others. One useful approach is to reliably determine the midline of the brain, which allows comparative analysis of the hemispheres and uncovers information on symmetry/asymmetry in the relevant planes of, for example, CT scans. Therefore, an effective method that is robust to various geometric deformations, artifacts, varying noise characteristics, and natural anatomical variability is sought.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Software Convergence, Seoul Women's University, Hwarango 621, Nowongu, Seoul, 01797, Republic of Korea.
In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix.
View Article and Find Full Text PDFGraefes Arch Clin Exp Ophthalmol
January 2025
College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Background: To establish an objective method for assessing plus disease severity in retinopathy of prematurity.
Methods: Six images of plus diseases that were color-coded according to severity and published in the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3) were analyzed. These images were individually processed, and the best-fit curve and vessel course in zone I were obtained using ImageJ software.
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