Literature DB >> 26707815

Digital breast tomosynthesis (DBT): a review of the evidence for use as a screening tool.

Fiona J Gilbert1, Lorraine Tucker2, Ken C Young3.   

Abstract

Breast screening with full-field digital mammography (FFDM) fails to detect 15-30% of cancers. This figure is higher for women with dense breasts. A new tomographic technique in mammography has been developed--digital breast tomosynthesis (DBT)--which allows images to be viewed in sections through the breast and has the potential to improve cancer detection rates. Results from retrospective reading studies comparing DBT with FFDM have been largely favourable with improvement in sensitivity and specificity. Increases in diagnostic accuracy have been reported as being independent of breast density; however there are mixed reports regarding the detection of microcalcification. Prospective screening studies using DBT with FFDM have demonstrated increased rates in cancer detection compared with FFDM alone. A reduction in false-positive recall rates has also been shown. Screening with the addition of DBT would approximately double radiation dose; however a simulated FFDM image can be generated from a DBT scan. The combination of simulated FFDM images and DBT is being evaluated within several studies and some positive results have been published. Interval cancer rates for the UK National Health Service Breast Screening Programme (NHSBSP) demonstrate the limited sensitivity of FFDM in cancer detection. DBT has the potential to increase sensitivity and decrease false-positive recall rates. It has approval for screening and diagnostics in several countries; however, there are issues with DBT as a screening tool including additional reading time, IT storage and connectivity, over-diagnosis, and cost effectiveness. Feasibility and cost-effectiveness trials are needed before the implementation of DBT in NHSBSP can be considered.
Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26707815     DOI: 10.1016/j.crad.2015.11.008

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  29 in total

1.  Comparison of synthetic and digital mammography with digital breast tomosynthesis or alone for the detection and classification of microcalcifications.

Authors:  Ji Soo Choi; Boo-Kyung Han; Eun Young Ko; Ga Ram Kim; Eun Sook Ko; Ko Woon Park
Journal:  Eur Radiol       Date:  2018-06-21       Impact factor: 5.315

2.  The Effect of Digital Breast Tomosynthesis Adoption on Facility-Level Breast Cancer Screening Volume.

Authors:  Christoph I Lee; Weiwei Zhu; Tracy L Onega; Jessica Germino; Ellen S O'Meara; Constance D Lehman; Louise M Henderson; Jennifer S Haas; Karla Kerlikowske; Brian L Sprague; Garth H Rauscher; Anna N A Tosteson; Jennifer Alford-Teaster; Karen J Wernli; Diana L Miglioretti
Journal:  AJR Am J Roentgenol       Date:  2018-09-20       Impact factor: 3.959

3.  Can the synthetic C view images be used in isolation for diagnosing breast malignancy without reviewing the entire digital breast tomosynthesis data set?

Authors:  Mark C Murphy; Louise Coffey; Ailbhe C O'Neill; Cecily Quinn; Ruth Prichard; Sorcha McNally
Journal:  Ir J Med Sci       Date:  2018-02-09       Impact factor: 1.568

4.  Verification of the accuracy of a hybrid breast imaging simulation framework for virtual clinical trial applications.

Authors:  Liesbeth Vancoillie; Nicholas Marshall; Lesley Cockmartin; Janne Vignero; Guozhi Zhang; Hilde Bosmans
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-22

5.  Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography.

Authors:  Andreas E Petropoulos; Spyros G Skiadopoulos; Anna N Karahaliou; Gerasimos A T Messaris; Nikolaos S Arikidis; Lena I Costaridou
Journal:  Med Biol Eng Comput       Date:  2019-12-07       Impact factor: 2.602

6.  Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction.

Authors:  Yao Lu; Heang-Ping Chan; Jun Wei; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Phys Med Biol       Date:  2017-09-15       Impact factor: 3.609

7.  Prospective validation of a blood-based 9-miRNA profile for early detection of breast cancer in a cohort of women examined by clinical mammography.

Authors:  Maria B Lyng; Annette R Kodahl; Harald Binder; Henrik J Ditzel
Journal:  Mol Oncol       Date:  2016-11-01       Impact factor: 6.603

8.  Characterization of Breast Masses in Digital Breast Tomosynthesis and Digital Mammograms: An Observer Performance Study.

Authors:  Heang-Ping Chan; Mark A Helvie; Lubomir Hadjiiski; Deborah O Jeffries; Katherine A Klein; Colleen H Neal; Mitra Noroozian; Chintana Paramagul; Marilyn A Roubidoux
Journal:  Acad Radiol       Date:  2017-06-21       Impact factor: 3.173

9.  Assessment of Radiologist Performance in Breast Cancer Screening Using Digital Breast Tomosynthesis vs Digital Mammography.

Authors:  Brian L Sprague; R Yates Coley; Karla Kerlikowske; Garth H Rauscher; Louise M Henderson; Tracy Onega; Christoph I Lee; Sally D Herschorn; Anna N A Tosteson; Diana L Miglioretti
Journal:  JAMA Netw Open       Date:  2020-03-02

10.  Availability Versus Utilization of Supplemental Breast Cancer Screening Post Passage of Breast Density Legislation.

Authors:  Mary W Marsh; Thad S Benefield; Sheila Lee; Michael Pritchard; Katie Earnhardt; Robert Agans; Louise M Henderson
Journal:  J Womens Health (Larchmt)       Date:  2020-09-22       Impact factor: 2.681

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