Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the PROSPR consortium
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Digital breast tomosynthesis (DBT) is emerging as the new standard of care for breast cancer screening based on improved cancer detection coupled with reductions in recall compared to screening with digital mammography (DM) alone. However, many prior studies lack follow-up data to assess false negatives examinations. The purpose of this study is to assess if DBT is associated with improved screening outcomes based on follow-up data from tumor registries or pathology. Retrospective analysis of prospective cohort data from three research centers performing DBT screening in the PROSPR consortium from 2011 to 2014 was performed. Recall and biopsy rates were assessed from 198,881 women age 40–74 years undergoing screening (142,883 DM and 55,998 DBT examinations). Cancer, cancer detection, and false negative rates and positive predictive values were assessed on examinations with one year of follow-up. Logistic regression was used to compare DBT to DM adjusting for research center, age, prior breast imaging, and breast density. There was a reduction in recall with DBT compared to DM (8.7 vs. 10.4 %, p < 0.0001), with adjusted OR = 0.68 (95 % CI = 0.65–0.71). DBT demonstrated a statistically significant increase in cancer detection over DM (5.9 vs. 4.4/1000 screened, adjusted OR = 1.45, 95 % CI = 1.12–1.88), an improvement in PPV1 (6.4 % for DBT vs. 4.1 % for DM, adjusted OR = 2.02, 95 % CI = 1.54–2.65), and no significant difference in false negative rates for DBT compared to DM (0.46 vs. 0.60/1000 screened, p = 0.347). Our data support implementation of DBT screening based on increased cancer detection, reduced recall, and no difference in false negative screening examinations.
KeywordsBreast cancer screening Mammography Digital breast tomosynthesis
Breast imaging-reporting and data system
Digital breast tomosynthesis
Dartmouth–Hitchcock health system in New Hampshire and Brigham and Women’s Hospital in Massachusetts
Generalized estimating equations
National Cancer Institute
Positive predictive value
Population-based research optimizing screening through personalized regimens
University of Pennsylvania
University of Vermont
The authors thank the participating PROSPR Research Centers for the data they have provided for this study. A list of the PROSPR investigators and contributing research staff are provided at: http://healthcaredelivery.cancer.gov/prospr/.
Dr. Conant had full access to all the data in the study and takes responsibility for the integrity.
This work was supported by the National Cancer Institute (NCI)-funded Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium (Grant numbers U01CA163304 to M.T., W.B.; U54CA163303 and P01 CA154292 to D.L.W., B.S., S.H.; U54CA163307 to A.N.A.T., T.O., J.H.; U54CA163313 to E.F.C., K.A., M.S.)
Compliance with ethical standards
Conflicts of interest
Author E.F.C. is a consultant and has been a lecturer for Hologic, Inc., Bedford MA and Siemen’s Healthcare. Author S.P.P. has been renumerated for participating as a reader for Biomedical Systems, St. Louis, MO. Author S.D.H. holds stock in Hologic, Inc. Bedford, MA. Author K.A. is a consultant to GlaxoSmithKline, Philadelphia, PA.
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