ArticleRadiographics : a review publication of the Radiological Society of North America, Inc
Digital Breast Tomosynthesis: Update on Technology, Evidence, and Clinical Practice.
Article in Radiographics : a review publication of the Radiological Society of North America, Inc. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Deep Learning Algorithms Versus Radiologists in Digital Breast Tomosynthesis for Breast Cancer Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Advances in Breast Cancer Diagnostics: From Screening to Precision Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Comparison of stationary digital chest tomosynthesis to portable chest radiographs: A patient study.PloS one · 2026Article
- Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.Breast cancer (Tokyo, Japan) · 2026Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network.Journal of breast imaging · 2025Review
- A Novel Phantom for Standardized Microcalcification Detection Developed Using a Crystalline Growth System.Tomography (Ann Arbor, Mich.) · 2025Article
- Imaging in Interventional Radiology: Breast Cryoablation.Seminars in interventional radiology · 2025Review
- An automated toolbox for microcalcification cluster modeling for mammographic imaging.Medical physics · 2025Article
- Diagnostic dilemma of lobular carcinoma: a mini-review of imaging modalities and the role of artificial intelligence and radiomics.Frontiers in oncology · 2025Review
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2024Article
- Budget impact analysis of introducing digital breast tomosynthesis in breast cancer screening in Italy.La Radiologia medica · 2024Article
- Modulation transfer function of digital breast tomosynthesis: a comparison of various edge devices.Radiological physics and technology · 2024Article
- X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.Physics in medicine and biology · 2024Article
- Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.Physics in medicine and biology · 2023Article
- Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Digital breast tomosynthesis (DBT) has been widely adopted in breast imaging in both screening and diagnostic settings. The benefits of DBT are well established. Compared with two-dimensional digital mammography (DM), DBT preferentially increases detection of invasive cancers without increased detection of in-situ cancers, maximizing identification of biologically significant disease, while mitigating overdiagnosis. The higher sensitivity of DBT for architectural distortion allows increased diagnosis of invasive cancers overall and particularly improves the visibility of invasive lobular cancers. Implementation of DBT has decreased the number of recalls for false-positive findings at screening, contributing to improved specificity at diagnostic evaluation. Integration of DBT in diagnostic examinations has also resulted in an increased percentage of biopsies with positive results, improving diagnostic confidence. Although individual DBT examinations have a longer interpretation time compared with that for DM, DBT has streamlined the diagnostic workflow and minimized the need for short-term follow-up examinations, redistributing much-needed time resources to screening. Yet DBT has limitations. Although improvements in cancer detection and recall rates are seen for patients in a large spectrum of age groups and breast density categories, these benefits are minimal in women with extremely dense breast tissue, and the extent of these benefits may vary by practice environment and by geographic location. Although DBT allows detection of more invasive cancers than does DM, its incremental yield is lower than that of US and MRI. Current understanding of the biologic profile of DBT-detected cancers is limited. Whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.