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False-Negative Review from the Mammography Audit: Refining Breast Imaging Practice.
Radiographics
February 2025
From the Washington University School of Medicine, Mallinckrodt Institute of Radiology, 510 S Kingshighway Blvd, St. Louis, MO 63110.
Annual review of false-negative (FN) mammograms is a mandatory and critical component of the Mammography Quality Standards Act (MQSA) annual mammography audit. FN review can help hone reading skills and improve the ability to detect cancers at mammography. Subtle architectural distortion, asymmetries (seen only on one view), small lesions, lesions with probably benign appearance (circumscribed regular borders), isolated microcalcifications, and skin thickening are the most common mammographic findings when the malignancy is visible at retrospective review of FN mammograms.
View Article and Find Full Text PDFTraditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for synthetic 2D mammography versus an artificial intelligence algorithm for digital breast tomosynthesis.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
Mammographic 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.
Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models.
Methods: We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS).
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.
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