@article{4650597fd6fc4e88907dd7d3dcaa7e30,
title = "Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening with Digital Breast Tomosynthesis in the United States",
abstract = "Digital breast tomosynthesis (DBT) is increasingly being used for routine breast cancer screening. We projected the long-term impact and cost-effectiveness of DBT compared to conventional digital mammography (DM) for breast cancer screening in the United States. Methods: Three Cancer Intervention and Surveillance Modeling Network breast cancer models simulated US women ages 40 years and older undergoing breast cancer screening with either DBT or DM starting in 2011 and continuing for the lifetime of the cohort. Screening performance estimates were based on observational data; in an alternative scenario, we assumed 4% higher sensitivity for DBT. Analyses used federal payer perspective; costs and utilities were discounted at 3% annually. Outcomes included breast cancer deaths, quality-adjusted life-years (QALYs), false-positive examinations, costs, and incremental cost-effectiveness ratios (ICERs). Results: Compared to DM, DBT screening resulted in a slight reduction in breast cancer deaths (range across models 0-0.21 per 1000 women), small increase in QALYs (1.97-3.27 per 1000 women), and a 24-28% reduction in false-positive exams (237-268 per 1000 women) relative to DM. ICERs ranged from $195 026 to $270 135 per QALY for DBT relative to DM. When assuming 4% higher DBT sensitivity, ICERs decreased to $130 533-$156 624 per QALY. ICERs were sensitive to DBT costs, decreasing to $78 731 to $168 883 and $52 918 to $118 048 when the additional cost of DBT was reduced to $36 and $26 (from baseline of $56), respectively. Conclusion: DBT reduces false-positive exams while achieving similar or slightly improved health benefits. At current reimbursement rates, the additional costs of DBT screening are likely high relative to the benefits gained; however, DBT could be cost-effective at lower screening costs.",
author = "Lowry, {Kathryn P.} and Amy Trentham-Dietz and Schechter, {Clyde B.} and Oguzhan Alagoz and Barlow, {William E.} and Burnside, {Elizabeth S.} and Conant, {Emily F.} and Hampton, {John M.} and Hui Huang and Karla Kerlikowske and Lee, {Sandra J.} and Miglioretti, {Diana L.} and Sprague, {Brian L.} and Tosteson, {Anna N.A.} and Yaffe, {Martin J.} and Stout, {Natasha K.}",
note = "Funding Information: This work was supported by the National Cancer Institute at the National Institutes of Health for CISNET (grant number U01 CA199218), the Population-Based Research Optimizing Screening through Personalized Regimens (PROSPR) Program (grant numbers U54 CA163303, U54 CA163307), and other projects (grant number P30 CA014520), and by a Research Fellow Grant from the Ralph Schlaeger Charitable Foundation (KPL). Data collection for model inputs from the Breast Cancer Surveillance Consortium (BCSC) was supported by National Cancer Institute grant P01 CA154292, contract HHSN261201100031C, and grant U54 CA163303. The collection of BCSC cancer and vital status data used in this study was supported in part by several state public health departments and cancer registries throughout the United States. For a full description of these sources, see https://www.bcsc-research.org/about/work-acknowledgement. Funding Information: Dr Lowry reports a research grant from GE Healthcare through her academic institution. Dr Burnside reports a research grant from Hologic, Inc. Dr Conant reports grant support and consulting work from Hologic, Inc, and iCAD, Inc. Dr Kerlikowske reports grant support from Google Sciences and unpaid consulting with Grail on the STRIVE study. Dr Miglioretti previously served as a member of the Hologic Scientific Advisory Board. Dr Yaffe reports a research collaboration with GE Healthcare and is a shareholder in Volpara Health Technologies. Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.",
year = "2020",
month = jun,
day = "1",
doi = "10.1093/jnci/djz184",
language = "English (US)",
volume = "112",
pages = "582--589",
journal = "Journal of the National Cancer Institute",
issn = "0027-8874",
publisher = "Oxford University Press",
number = "6",
}