Purpose: To evaluate the effect of old mammograms on the specificity and sensitivity of radiologists in mammography screening.
Material And Methods: One hundred and fifty sets of screening mammograms were examined by 3 experienced screeners twice: once without and once in comparison with older mammograms. The films came from a population-based screening done during the first half of 1994 and comprised all 35 cancers detected during screening in 1994, 12/24 interval cancers, 14/34 cancers detected in the following screening and 89 normal mammograms.
Results: Without old mammograms, the screeners detected an average of 40.3 cancers (range 37-42), with a specificity of 87% (85-88%). With old mammograms, the screeners detected 37.7 cancers (range 34-42) with a specificity of 96% (94-99%). The change in detection rate was not significant. However, the increase in specificity was significant for each screener (p = 0.0002-0.03).
Conclusion: Mammography screening with old mammograms available for comparison decreased the false-positive recall rate. The effect on sensitivity, however, was unclear.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
The Screening Tomosynthesis Trial with Advanced Reader Methods (STREAM): design and rationale of a population-based breast cancer screening trial.
Eur Radiol
January 2025
Radboud University Medical Center, IQ Health science department, Nijmegen, The Netherlands.
Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.
Materials And Methods: The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50-72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT.
Traditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for synthetic 2D mammography versus an artificial intelligence algorithm for digital breast tomosynthesis.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
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 PDFEligibility 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.
Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.
Nat Med
January 2025
Institute for Social Medicine and Epidemiology, University of Lübeck, Lubeck, Germany.
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 PDFWant 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!