Purpose: There is insufficient evidence to recommend mammography for women >75 years. Guidelines recommend that older women be informed of the uncertainty of benefit and potential for harm, especially for women with short life expectancy. However, few older women are informed of harms of screening and many with short life expectancy are screened. Therefore, we aim to test whether a mammography screening decision aid (DA) for women >75 years affects their use of mammography, particularly for women with <10 year life expectancy.
Methods/design: The DA is a self-administered pamphlet that includes information on screening outcomes, tailored information on breast cancer risk, health, life expectancy, and competing mortality risks, and includes a values clarification exercise. We are conducting a large cluster randomized controlled trial (RCT) of the DA with the primary care provider (PCP) as the unit of randomization to evaluate its efficacy. We plan to recruit 550 women 75-89 years from 100 PCPs to receive either the mammography DA or a pamphlet on home safety for older adults (control arm) before a visit with their PCP, depending on their PCP's randomization assignment. The primary outcome is receipt of mammography screening assessed through chart abstraction. Secondary outcomes include effect of the DA on older women's screening intentions, knowledge, and decisional conflict, and on documented discussions about mammography by their PCPs. We will recruit women from 5 Boston-based primary care practices (3 community-based internal medicine practices and 2 academic practices), and 2 North Carolina-based academic primary care practices.
Discussion: It is essential that we test the DA in a large RCT to determine if it is efficacious and to substantiate the need for broad translation into clinical practice. Our DA has the potential to improve health care utilization and care in a manner dictated by patient preferences.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517685 | PMC |
| http://dx.doi.org/10.4172/2167-0870.1000191 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Background parenchymal enhancement in CEM and MRI: Is there always a high agreement?
Eur J Radiol
December 2024
Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1180, Austria.
Introduction: Background parenchymal enhancement (BPE) refers to the physiological enhancement of breast fibroglandular tissue. This study aimed to determine the agreement of BPE evaluation between contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) and investigate potential confounders.
Materials And Methods: This retrospective, IRB-approved study included women recalled from screening or with inconclusive findings on mammography and/or ultrasound, who underwent both CEM and MRI between 2018 and 2022.
Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.
Front Immunol
December 2024
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Objective: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.
Methods: A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024.
Perceived everyday discrimination, socioeconomic status, and mammography behavior.
Sci Rep
December 2024
Department of Health Disparities Research, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Black women (BW) experience age-adjusted breast cancer mortality rates that are 40% higher than White women. Although, screening rates for breast cancer are similar between White and Black women, differences in mammography utilization exist among women with lower socioeconomic status (SES). Moreover, perceived everyday discrimination (PED) has been shown to have an inverse relationship on health screening behavior among BW.
View Article and Find Full Text PDFChanges to the US Preventive Services Task Force Screening Guidelines and Incidence of Breast Cancer.
JAMA Netw Open
December 2024
Department of Surgery, University of Vermont, Burlington.
Importance: The 2009 US Preventive Services Task Force breast cancer screening guideline changes led to decreases in screening mammography, raising concern about potential increases in late-stage disease and more invasive surgical treatments.
Objective: To investigate the incidence of breast cancer by stage at diagnosis and surgical treatment before and after the 2009 guideline changes.
Design, Setting, And Participants: This population-based, epidemiologic cohort study of women aged 40 years or older used 2004 to 2019 data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program.
CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.
Tomography
December 2024
Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
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!