Mammography

Researchers are studying a new approach to reduce unnecessary screening mammograms in women aged 75 and older. This trial focuses on decreasing overuse of mammography, which may cause more harm than benefit in older women due to factors like shorter life expectancy and risks from follow-up procedures. The study aims to test a multilevel strategy involving patients, providers, and healthcare organizations to better align mammography use with current guidelines and patient needs. The study compares two groups: one receiving enhanced usual care with organizational support such as provider education and consensus-building task forces, and another receiving this plus additional provider and patient interventions. Providers in the intervention group receive educational newsletters, and patients receive a brochure encouraging discussions with their providers about the appropriateness of continuing mammograms. Organizational components apply to all participants across selected clinics. Participants will be monitored for 18 months to assess screening mammography overuse, provider ordering behavior, and mammography screening discussions. Data will be collected through scheduled primary care visits and healthcare records. The study involves a cluster randomized controlled design at the provider level and includes diverse older women. Safety and study outcomes will be tracked throughout the trial period.

Researchers are conducting a randomized controlled trial to compare the outcomes of screening mammography exams interpreted with and without the help of an FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings. The study aims to determine if using AI improves breast cancer detection and recall rates during screening. This trial includes all adult patients undergoing screening mammography and all interpreting radiologists across six regional health systems. During the trial, each 3D screening mammogram will be randomly assigned to either the intervention group, where radiologists receive assistance from the AI decision-support tool, or the usual care group, where radiologists interpret the images alone. Randomization happens at the exam level immediately after image acquisition, and patients returning for screening in the second year will be re-randomized. Radiologists will see AI information only when it is available during interpretation, but they maintain full control over their final diagnosis. Participants will undergo routine screening mammograms, with data collected on cancer detection rates within 90 days and recall rates over about one year. Additional measures include false-positive rates, interval cancer rates, and trust in AI over several years. The study plans to assess these outcomes across approximately 400,000 screening exams, with monitoring done through the participating health systems. Radiologists and patients will contribute perspectives on AI use in medical imaging during the trial period.

Researchers are evaluating a new noninvasive system that uses artificial intelligence (AI) to analyze multiple types of imaging, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), to help diagnose and predict outcomes for digestive diseases. This observational study collects and analyzes retrospective imaging, endoscopic, and clinical data from 21 centers in China to build and improve the AI model. The model will then be tested prospectively in two centers and its ability to locate lesions will be checked in real-world endoscopy settings. The study involves using a virtual endoscopy model to assist in diagnosis by integrating and analyzing multimodal imaging features. The AI system is designed to support diagnosis without any invasive procedures. The study will confirm the model's accuracy and effectiveness through retrospective data, prospective validation, and real-world deployment in clinical environments. Participants will contribute data from their imaging and endoscopic exams, with at least one technically adequate CT or MR scan and a high-quality colonoscopy performed within one month of imaging. Researchers will assess the AI model's diagnostic performance by measuring the area under the ROC curve (AUC) over six months. The study includes ongoing monitoring of data quality and imaging accuracy to ensure reliable validation of the AI system.

Researchers are evaluating the use of Artificial Intelligence (AI)-assisted imaging technologies, including AI-assisted breast ultrasound and AI-assisted mammography, in population-based breast cancer screening. The study aims to improve the effectiveness and feasibility of breast cancer screening by addressing key challenges in large-scale screening. This project will provide scientific evidence for the implementation of AI-assisted imaging in breast cancer detection and explore optimized screening strategies for the Chinese population. Participants will be divided into two groups based on district clusters. The intervention group will receive combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will undergo routine screening with initial breast ultrasound followed by mammography if needed. The trial will compare these approaches across various technical and cost-effectiveness aspects using data from population screening practices. During the study, women will be followed for one year after screening to measure outcomes such as the incidence of early-stage breast cancer and the detection rate of suspicious breast lesions, including masses and calcifications. Researchers will monitor these results to assess the impact of AI-assisted screening compared to routine methods. The total participation period will cover enrollment, screening, and a one-year follow-up to ensure comprehensive evaluation.

Researchers are developing a diagnostic model to help distinguish breast cancer from benign breast diseases by studying methylation markers in blood and tissue samples. This observational study focuses on collecting samples from participants diagnosed with either breast cancer or benign breast conditions to analyze whole-genome methylation patterns. The goal is to find specific methylation markers that can assist in diagnosing breast nodules more accurately. The study involves two groups: one with participants having breast cancer and another with participants having benign breast diseases. Blood and tumor tissue samples will be collected from the cancer group, while blood and tissue samples will be collected from the benign group. These samples will undergo whole-genome methylation sequencing to identify diagnostic markers and develop a model that can differentiate malignant from benign breast nodules. Participants will provide tissue and blood samples, and their molecular subtyping results will be confirmed. Researchers will examine DNA methylation profiles over 12 months, evaluating the performance of methylation markers and integrating them with imaging results. The study aims to create a combined diagnostic model with high specificity and sensitivity. The total study duration for each participant is 12 months, during which safety and study completion will be monitored.

This research aims to evaluate the MAMMOMAT B.brilliant system, a new wide-angle digital breast tomosynthesis imaging device, in women undergoing breast cancer screening with suspicious calcifications. The study compares this novel system to the standard MAMMOMAT Revelation system, both FDA approved, to assess image quality and visibility of calcifications. The study is observational and involves women 18 years or older who require additional diagnostic imaging after screening. Participants will receive standard diagnostic mammography using the same machine as their screening exam, either the MAMMOMAT B.brilliant or the MAMMOMAT Revelation. In addition, they will have two research images taken on the alternate system. The research scans will include compression of the affected breast for 5-8 or 25 seconds in two views to collect data on image quality and calcification visibility. During the study, women will undergo these imaging procedures and may complete patient surveys assessing pain and comfort. Researchers will compare the two imaging systems over an 18-month period, focusing on the accuracy of detecting calcifications and patient experience. All diagnostic decisions will follow standard care protocols. The total study duration for each participant extends to 18 months from enrollment.

This observational study examines how breast density affects the accuracy and outcomes of mammographic screening for breast cancer within the regional "Prevenzione Serena" program at ASL CN2. It focuses on women aged 45 to 75 years who underwent routine screening between September 2023 and May 2024. The study evaluates the association between breast density categories (BI-RADS A-D) and the frequency of recalls for additional diagnostic tests, aiming to understand how dense breast tissue impacts detection and false-positive rates. The research includes an internal validation of Insight BD, an automated software used to measure breast density. This validation involves technical testing with a mammography phantom and comparisons of the software's BI-RADS classifications with assessments from two radiologists. No treatments or interventions are given, as data are collected retrospectively from clinical records. The study also explores how factors like menopausal status, hormone therapy, and family history relate to breast density and screening outcomes. Participants' data will be analyzed for recall rates for second-level exams such as ultrasound or MRI, false-positive findings, and breast density distribution. Researchers will monitor the software's accuracy and agreement with radiologists' readings. The primary outcome is the recall rate by breast density category during the screening period. The study aims to inform future personalized screening guidelines and improve breast cancer detection strategies for women with dense breasts.

This research aims to evaluate how bundled familial cancer risk assessment combined with navigation for multiple cancers (colorectal and breast) compares to navigation for breast cancer alone. The study also examines usual care referral to genetic services versus pretest education plus usual care referral. Researchers will study how effective bundled multicancer navigation is and for whom it works best through a detailed patient- and organization-level process evaluation across multiple sites. Participants are women referred for screening navigation who will receive either multicancer navigation or single breast cancer navigation, with a wait list control for colorectal cancer screening referral. Those eligible for genetic services will be randomized to receive combinations of multicancer or single cancer navigation paired with either usual care genetic referral or pretest education plus usual care referral. Navigation involves education, barrier assessment, scheduling, reminders, and documentation of screening completion. Participants not screened for colorectal cancer after six months in the single navigation arm will receive delayed colorectal cancer screening navigation. Women in the study will complete baseline assessments and be randomized to one of the study arms. Researchers will monitor receipt of colorectal and breast cancer screenings six months after navigation completion. Assessments include documentation of navigation steps and participant follow-up. The study plans to recruit 820 women, retaining 780 for analysis over two years. A mixed-methods process evaluation will be conducted alongside the trial to understand the implementation and effectiveness of the interventions.

This research aims to compare two types of digital breast tomosynthesis (DBT) systems: narrow-angle and wide-angle. The study focuses on evaluating which system provides better visibility of masses and architectural distortions in breast imaging. It also gathers feedback from radiologists, technologists, and participants regarding the imaging experience and comfort. Participants will receive follow-up imaging using an FDA-approved DBT system that differs from their routine screening system. For example, if their standard screening used narrow-angle DBT, the study imaging will use wide-angle DBT, and vice versa. These images will be used for diagnosis and to guide further care. Throughout the study, participants will be monitored for safety and any adverse events for about one year. Researchers will also collect radiologist feedback, technologist experiences, and participant comfort survey responses. The study involves women undergoing standard screening mammography who can provide informed consent.

Researchers are evaluating how well women may choose to use Contrast-enhanced Spectral Mammography (CESM) as their yearly breast cancer screening compared to the standard 2-D or 3-D mammogram. This study focuses on women with heterogeneous or dense breast tissue reported on a previous mammogram and who are not at high risk for breast cancer. The study aims to understand patient experiences and decision-making factors such as age, education, and lifetime risk in adopting CESM screening. The study involves 210 women who will undergo a screening CESM exam, which includes an iodine-based contrast injection followed by two images to detect areas of increased blood flow potentially indicating cancer. The CESM exam will replace their usual annual breast screening mammogram. Participants will complete questionnaires before and after the CESM exam to assess their attitudes, concerns about contrast use, and past mammogram experiences. Participants will be involved for approximately 16 months, during which researchers will evaluate patient experiences related to CESM screening. Surveys will assess willingness to undergo CESM and its association with demographic and risk factors. Abnormal findings will be managed independently. This study includes monitoring patient feedback and learning how CESM may be integrated into routine breast cancer screening for women with dense breast tissue.