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Purpose To evaluate the early implementation of synthesized two-dimensional (s2D) mammography in a population screened entirely with s2D and digital breast tomosynthesis (DBT) (referred to as s2D/DBT) and compare recall rates and cancer detection rates to historic outcomes of digital mammography combined with DBT (referred to as digital mammography/DBT) screening. Materials and Methods This was an institutional review board-approved and HIPAA-compliant retrospective interpretation of prospectively acquired data with waiver of informed consent. Compared were recall rates, biopsy rates, cancer detection rates, and radiation dose for 15 571 women screened with digital mammography/DBT from October 1, 2011, to February 28, 2013, and 5366 women screened with s2D/DBT from January 7, 2015, to June 30, 2015. Two-sample z tests of equal proportions were used to determine statistical significance. Results Recall rate for s2D/DBT versus digital mammography/DBT was 7.1% versus 8.8%, respectively (P < .001). Biopsy rate for s2D/DBT versus digital mammography/DBT decreased (1.3% vs 2.0%, respectively; P = .001). There was no significant difference in cancer detection rate for s2D/DBT versus digital mammography/DBT (5.03 of 1000 vs 5.45 of 1000, respectively; P = .72). The average glandular dose was 39% lower in s2D/DBT versus digital mammography/DBT (4.88 mGy vs 7.97 mGy, respectively; P < .001). Conclusion Screening with s2D/DBT in a large urban practice resulted in similar outcomes compared with digital mammography/DBT imaging. Screening with s2D/DBT allowed for the benefits of DBT with a decrease in radiation dose compared with digital mammography/DBT. RSNA, 2016 An earlier incorrect version of this article appeared online. This article was corrected on August 11, 2016.
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http://dx.doi.org/10.1148/radiol.2016160366 | DOI Listing |
J Radiol Prot
September 2025
Radiological Physics & Advisory Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra, INDIA.
Purpose This foundational study aims to support the development of regional Diagnostic Reference Levels (DRLs) for mammography screening in India by estimating Mean Glandular Dose (MGD) using appropriate breast-equivalent phantoms across Computed Mammography (CR), Digital Mammography (DR), and Digital Breast Tomosynthesis (DBT) systems. Additionally, system-displayed MGD values were compared with calculated MGDs to evaluate their accuracy for routine clinical use. Methods A total of 5,000 mammographic views were collected across CR, DR, and DBT units.
View Article and Find Full Text PDFAJR Am J Roentgenol
September 2025
Department of Radiology, University of California, Los Angeles, 200 UCLA Medical Plaza, Los Angeles, CA, 90095.
By reliably classifying screening mammograms as negative, artificial intelligence (AI) could minimize radiologists' time spent reviewing high volumes of normal examinations and help prioritize examinations with high likelihood of malignancy. To compare performance of AI, classified as positive at different thresholds, with that of radiologists, focusing on NPV and recall rates, in large population-based digital mammography (DM) and digital breast tomosynthesis (DBT) screening cohorts. This retrospective single-institution study included women enrolled in the observational population-based Athena Breast Health Network.
View Article and Find Full Text PDFCurr Probl Diagn Radiol
August 2025
Dasa /Alta Excelência Diagnóstica, Radiodiagnóstico por Imagem, SP, Brazil.
Background And Purpose: Accurate preoperative staging is essential for guiding surgical planning and optimizing outcomes in early-stage breast cancer. Magnetic resonance imaging (MRI) is considered the gold standard but is often limited by cost and availability. This study aimed to prospectively compare the diagnostic performance of full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and MRI for tumor detection and size estimation in patients eligible for upfront surgery.
View Article and Find Full Text PDFTomography
July 2025
Department of Biomedical Engineering, University of Houston, 4800 Calhoun Rd, Houston, TX 77004, USA.
There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications.
View Article and Find Full Text PDFRadiography (Lond)
August 2025
Department of Medical Imaging, Faculty of Applied Health Science, The Hashemite University, Zarqa, 13133, Jordan.
Introduction: Diagnostic reference levels (DRLs) are one of the most important radiation protection methods in medical imaging to help optimize radiation exposure during imaging procedures. The study aimed to establish national DRL for digital mammography (DM) and digital breast tomosynthesis (DBT) in Jordan, considering variations in compressed breast thickness (CBT) and breast density.
Methodology: The exposure parameters, average glandular dose (AGD), CBT, breast density, and viewing projection were extracted.