Digital breast tomosynthesis (DBT) has improved conventional mammography by increasing cancer detection while reducing recall rates. However, these benefits come at the cost of increased radiation dose. Synthesized mammography (s2D) has been developed to provide the advantages of DBT with nearly half the radiation dose. Since its F.D.A. approval, multiple studies have evaluated the clinical performance of s2D. In clinical practice, s2D images are not identical to conventional 2D images and are designed for interpretation with DBT as a complement. This article reviews the present literature to assess whether s2D is a practical alternative to conventional 2D, addresses the differences in mammographic appearance of findings, and provides suggestions for implementation into clinical practice.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023509 | PMC |
| http://dx.doi.org/10.3390/diagnostics8020022 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Screening for breast cancer: a systematic review update to inform the Canadian Task Force on Preventive Health Care guideline.
Syst Rev
December 2024
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Objective: This systematic review update synthesized recent evidence on the benefits and harms of breast cancer screening in women aged ≥ 40 years and aims to inform the Canadian Task Force on Preventive Health Care's (CTFPHC) guideline update.
Methods: We searched Ovid MEDLINE® ALL, Embase Classic + Embase and Cochrane Central Register of Controlled Trials to update our searches to July 8, 2023. Search results for observational studies were limited to publication dates from 2014 to capture more relevant studies.
Comparison of Full-Field Digital Mammography with Synthesized Mammography from Tomosynthesis in a Diagnostic population: Prospective Study.
Indian J Radiol Imaging
January 2025
Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India.
Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis. To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population.
View Article and Find Full Text PDFThe Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.
Cancers (Basel)
November 2024
National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.
Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images.
View Article and Find Full Text PDFClinical guidelines for the management of mammographic density: a systematic review of breast screening guidelines worldwide.
JNCI Cancer Spectr
November 2024
Sydney Health Literacy Lab, School of Public Health, Faculty of Medicine and Health, The University of Sydney, NSW, Australia.
Article Synopsis
- - High breast density increases the risk of breast cancer and reduces the effectiveness of mammography, prompting this review to analyze clinical guidelines for screening women with dense breasts.
- - The systematic review included 23 guidelines from 2014 to 2024, finding variability in quality and recommendations: most suggest annual or biennial mammograms for women over 40, while a few endorse advanced screening methods like tomosynthesis or MRI.
- - There's a lack of consensus on supplemental screening practices, with many guidelines relying on low-quality evidence; improvements in the quality of future guidelines are necessary as the understanding of screening benefits continues to evolve.
Artificial intelligence in mammography: a systematic review of the external validation.
Rev Bras Ginecol Obstet
October 2024
Universidade Federal de Minas Gerais Belo HorizonteMG Brazil Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Article Synopsis
- The study aimed to review how different Artificial Intelligence algorithms perform in validating mammography for breast cancer screening through a systematic analysis of existing research.
- After searching major medical databases and filtering through thousands of articles, 30 relevant studies were selected for analysis over the past decade.
- The findings indicated that AI algorithms can match radiologists in accuracy and even improve mammogram evaluations when used alongside them, aiding both experienced and less experienced medical professionals in detecting potential lesions.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!