Trial reportEuropean radiology2017
Accuracy and reading time for six strategies using digital breast tomosynthesis in women with mammographically negative dense breasts.
Trial report in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 32 citations in OpenAlex.
- The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis.Frontiers in oncology · 2025Article
- Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence.European radiology · 2023Article
- Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs.Journal of imaging · 2022Article
- Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object.Journal of imaging · 2022Article
- Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis.Scientific reports · 2020Article
- Cost-effectiveness of Digital Breast Tomosynthesis in Population-based Breast Cancer Screening: A Probabilistic Sensitivity Analysis.Radiology · 2020Article
- Double reading in breast cancer screening: considerations for policy-making.The British journal of radiology · 2020Review
- [Artificial intelligence in the diagnosis of breast cancer : Yesterday, today and tomorrow].Der Radiologe · 2020Review
- Mass Detection and Segmentation in Digital Breast Tomosynthesis Using 3D-Mask Region-Based Convolutional Neural Network: A Comparative Analysis.Frontiers in molecular biosciences · 2020Article
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.Radiology · 2019Review
- Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare six strategies using digital breast tomosynthesis in women with mammographically negative dense breasts. MATERIALS AND
methodsThis is a substudy of the 'ASTOUND' trial. 163 women who underwent tomosynthesis with synthetically reconstructed projection images (S-2D) inclusive of 13 (7.9%) cases diagnosed with breast cancer at histopathology after surgery were evaluated. Accuracy measures and screen-reading time of six reading strategies were assessed: (A) Single reading of S-2D alone, (B) single reading of tomosynthesis alone, (C) single reading of joint interpretation of tomosynthesis + S-2D, (D) double-reading of S-2D alone, (E) double reading of tomosynthesis alone, (F) double reading of joint interpretation of tomosynthesis + S-2D.
resultsThe median age of the patients was 53 years (range, 36-88 years). The highest global accuracy was obtained with double reading of tomosynthesis + S2D (F) with an AUC of 0.979 (p<0.001) and a mean reading time of 154 s versus 34 s for the fastest strategy (single reading of S-2D alone). The AUCs for the other five strategies did not differ from each other.
conclusionDouble reading of tomosynthesis+ S2D had the best accuracy of six screen-reading strategies although it had the longest reading time. KEY POINTS: • Tomosynthesis acquisitions are progressively implemented with reconstructed synthesized 2D images • Double reading using S-2D plus tomosynthesis had the highest global accuracy (p<0.001). • Double reading of S-2D plus tomosynthesis increased reading time.
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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.