Screening mammography saves lives. The mainstay of screening has been mammography. Multiple alternative options, however, for supplemental imaging are now available. Some are just improved anatomic delineation whereas others include physiology added to anatomy. A third group (molecular imaging) is purely physiologic. This article describes and compares the available options and for which patient populations they should be used.
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http://dx.doi.org/10.1016/j.cpet.2018.02.001 | DOI Listing |
Nat Med
January 2025
Institute for Social Medicine and Epidemiology, University of Lübeck, Lubeck, Germany.
Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an observational, multicenter, real-world, noninferiority, implementation study comparing the performance of AI-supported double reading to standard double reading (without AI) among women (50-69 years old) undergoing organized mammography screening at 12 sites in Germany. Radiologists in this study voluntarily chose whether to use the AI system.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 50050, Taiwan.
: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application.
View Article and Find Full Text PDFDiagnostics (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.
Cancers (Basel)
December 2024
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan 33382, Taiwan.
Contrast-enhanced mammography (CEM) uses intermittent dual-energy (low- and high-energy) exposures to produce low-energy mammograms and recombine enhanced images after the administration of iodized contrast medium, which provides more detailed information to detect breast cancers by using the features of morphology and abnormal uptake. In this article, we reviewed the literature to clarify the clinical applications of CEM, including (1) the fundamentals of CEM: the technique, radiation exposure, and image interpretation; (2) its clinical uses for cancer diagnosis, including problem-solving, palpable mass, suspicious microcalcification, architecture distortion, screening, and CEM-guided biopsy; and (3) the concerns of surgical oncology in pre-operative and neoadjuvant chemotherapy assessments. CEM undoubtedly plays an important role in clinical practice.
View Article and Find Full Text PDFIntroduction 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).
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