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Lymph Node Adiposity and Metabolic Dysfunction-Associated Steatotic Liver Disease.
Biomedicines
January 2025
Radiology Department, Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA.
: Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as the most common chronic liver disease, is soon to be the leading indication for liver transplantation; however, the diagnosis may remain occult for decades. There is a need for biomarkers that identify patients at risk for MASLD and patients at risk for disease progression to optimize patient management and outcomes. Lymph node adiposity (LNA) is a novel marker of adiposity identified within axillary lymph nodes on screening mammography.
View Article and Find Full Text PDFMammographic Vascular Microcalcifications as a Surrogate Parameter for Coronary Heart Disease: Correlation to Cardiac Computer Tomography and Proposal of a Classification Score.
Diagnostics (Basel)
December 2024
Institute of Diagnostic and Interventional Radiology, GZO Regional Health Center, 8620 Wetzikon, Switzerland.
Objective: This study develops a BI-RADS-like scoring system for vascular microcalcifications in mammographies, correlating breast arterial calcification (BAC) in a mammography with coronary artery calcification (CAC), and specifying differences between microcalcifications caused by BAC and microcalcifications potentially associated with malignant disease.
Materials And Methods: This retrospective single-center cohort study evaluated 124 consecutive female patients (with a median age of 57 years). The presence of CAC was evaluated based on the Agatston score obtained from non-enhanced coronary computed tomography, and the calcifications detected in the mammography were graded on a four-point Likert scale, with the following criteria: (1) no visible or sporadically scattered microcalcifications, (2) suspicious microcalcification not distinguishable from breast arterial calcification, (3) minor breast artery calcifications, and (4) major breast artery calcifications.
Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.
Radiol Phys Technol
January 2025
Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
Article Synopsis
- Self-supervised learning (SSL) is increasingly being used in medical imaging, particularly through methods like the Jigsaw puzzle task, which helps models learn features and relationships within images without needing labeled data.
- The study evaluated different pre-training methods on mammographic images from the Chinese Mammography Database, comparing the effectiveness of models that used Jigsaw puzzles, ImageNet tasks, or no pre-training at all in detecting breast cancer.
- Results showed that models utilizing the Jigsaw puzzle task achieved the highest area under the curve (AUC) scores, indicating its potential to improve classification accuracy even with limited data.
Low breast density is associated with epicardial adipose tissue volume and coronary artery disease.
Clin Imaging
January 2025
Monash University, Wellington Rd, Clayton 3800, Melbourne, Victoria, Australia; Victorian Heart Hospital, Blackburn Rd, Clayton 3800, Melbourne, Victoria, Australia; Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia. Electronic address:
Article Synopsis
- The study investigates the link between epicardial adipose tissue volume (EATv), breast density, and coronary artery disease (CAD) in women, highlighting the correlation of low breast density with increased CAD risk.
- It involved 153 women who underwent both coronary computed tomography angiogram (CCTA) and mammography, revealing that those with low breast density had higher EATv and a greater prevalence of hypertension and BMI.
- Results suggest low breast density is a significant predictor of CAD presence, independent of EATv and various cardiovascular risk factors, indicating its potential as an early risk assessment tool for CAD in women.
Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications.
Heliyon
October 2024
Department of Computer Science, Al-Quds University, Palestine.
Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management.
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