Digital Mammography versus Digital Mammography Plus Tomosynthesis for Breast Cancer Screening: The Reggio Emilia Tomosynthesis Randomized Trial.

Radiology

From the Radiology Unit (P.P., V.I., V.G., S.R., R.V.), Medical Physics Unit (A.N.), Epidemiology Unit (P.G.R.), Scientific Directorate (L.B., S.C.), and Screening Coordinating Centre (C.C.), AUSL Reggio Emilia, IRCCS, Via Amendola 2, Reggio Emilia 42122, Italy.

Published: August 2018

Purpose To compare digital mammography (DM) plus digital breast tomosynthesis (DBT) versus DM alone for breast cancer screening in the Reggio Emilia Tomosynthesis trial, a two-arm test-and-treat randomized controlled trial. Materials and Methods For this trial, eligible women (45-70 years old) who previously participated in the Reggio Emilia screening program were invited for mammography. Consenting women were randomly assigned 1:1 to undergo DBT+DM or DM (both of which involved two projections and double reading). Women were treated according to the decision at DBT+DM. Sensitivity, recall rate, and positive predictive value (PPV) at baseline were determined; the ratios of these rates for DBT+DM relative to DM alone were determined. Results From March 2014 to March 2016, 9777 women were recruited to the DM+DBT arm of the study, and 9783 women were recruited to the DM arm (mean age, 56.2 vs 56.3 years). Recall was 3.5% in both arms; detection was 4.5 per 1000 (44 of 9783) and 8.6 per 1000 (83 of 9777), respectively (+89%; 95% confidence interval [CI]: 31, 72). PPV of the recall was 13.0% and 24.1%, respectively (P = .0002); 72 of 80 cancers found in the DBT+DM arm and with complete DBT imaging were positive at least at one DBT-alone reading. The greater detection rate for DM+DBT was stronger for ductal carcinoma in situ (+180%, 95% CI: 1, 665); it was notable for small and medium invasive cancers, but not for large ones (+94 [95% CI: 6, 254]; +122 [95% CI: 18, 316]; -12 [95% CI: -68, 141]; for invasive cancers < 10 mm, 10-19 mm, and ≥ 20 mm, respectively). Conclusion DBT+DM depicts 90% more cancers in a population previously screened with DM, with similar recall rates.

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.2018172119DOI Listing

Publication Analysis

Top Keywords

digital mammography
12
reggio emilia
12
breast cancer
8
cancer screening
8
screening reggio
8
emilia tomosynthesis
8
women recruited
8
invasive cancers
8
women
5
dbt+dm
5

Similar Publications

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

BMC Med Imaging

January 2025

Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

View Article and Find Full Text PDF

Eligibility for and reach of the National Breast and Cervical Cancer Early Detection Program, 2018-2021.

Cancer Causes Control

January 2025

Department of Health Policy and Management, Winship Cancer Center, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30030, USA.

Purpose: The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) provides access to timely breast and cervical cancer screening and diagnostic services to women who have low incomes and are uninsured or underinsured. Documenting the number of women eligible and the proportion of eligible women who receive NBCCEDP-funded services is important for identifying opportunities to increase screening and diagnostic services among those who would not otherwise have access.

Methods: Using the Census Bureau's Small Area Health Insurance Estimates data, we estimated the number of women who met the NBCCEDP eligibility criteria based on age, income, and insurance status.

View Article and Find Full Text PDF

Purpose: The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance.

Approach: We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods.

View Article and Find Full Text PDF

Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an observational, multicenter, real-world, noninferiority, implementation study comparing the performance of AI-supported double reading to standard double reading (without AI) among women (50-69 years old) undergoing organized mammography screening at 12 sites in Germany. Radiologists in this study voluntarily chose whether to use the AI system.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!