Breast Cancer Screening

Breast cancer is the most common cancer among women worldwide. This randomized controlled trial aims to evaluate the effect of an Instagram- and podcast-based intervention on women's breast cancer awareness, health beliefs, and breast cancer screening behaviors among women aged 20-39 years.

The goal of this study is to evaluate the impact of a multi-level intervention on cancer prevention and social determinants of health outcomes. This partnership between academic and community-based researchers aims to link community health centers and communities in ways that maximize both civic engagement and participation in cancer control care. The system-level intervention consists of two components: (1) provision of community health center systems-level tools for cancer screening (breast, cervical, colorectal) and tobacco cessation treatment gaps; and (2) digital access screening and navigation. These interventions will be delivered via usual care.

The goal of this study is to test bundled familial cancer risk assessment + multicancer (colorectal + breast) vs. single (breast) cancer navigation, using a wait list control for colorectal cancer screening referral and navigation. Among those eligible, this study will test usual care referral to genetic services vs. pretest education + usual care referral. The study also will assess how bundled multicancer navigation works and for whom it is most effective through a multisite, mixed-methods patient- and organization-level process evaluation.

This is an observational study to evaluate the MAMMOMAT B.brilliant system. All diagnostic decisions are made by the treating radiologist based upon standard of care clinical imaging acquired on FDA approved devices

This randomized controlled trial investigates the impact of a social media-based educational program on women's knowledge, attitudes, and digital health literacy regarding breast, cervical, and colorectal cancer screenings. The intervention is based on the Socio-Ecological Model and aims to improve awareness and screening participation. Participants in the intervention group receive daily educational content for 8 weeks via social media platforms (WhatsApp/Instagram), while the control group receives two standard online education sessions via Microsoft Teams app. The study includes 132 women aged 30 to 70 in Türkiye who have not previously participated in cancer screenings. The primary outcomes include changes in cancer knowledge, attitudes, and digital health literacy levels measured by validated scales.

The purpose of this research study is to test a new process for diagnosing breast cancer by examining changes to your DNA that can be detected from a blood test. The information we learn by doing this study could potentially help people in the future. Participants in this study will have blood samples collected, have their medical records reviewed by study personnel and fill out questionnaires at different time points during the study. Blood sample collection will occur during normal routine clinic visits. Participation in this study will last approximately 5 years.

This study aims to develop and evaluate a theory-based educational intervention to improve cancer screening participation among Syrian migrants in Turkey. The study will also develop a Cancer Protection Motivation Scale (CPMS) to measure individuals' motivation toward cancer screening. Using a mixed-methods design, the study will explore barriers, beliefs, and behaviors related to breast, cervical, and colorectal cancer screening. The effectiveness of the intervention will be assessed using a pretest-posttest design. The findings are expected to support culturally appropriate strategies to improve cancer screening uptake among migrant populations.

A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.

This retrospective, observational study aims to evaluate how breast density affects the accuracy and outcomes of mammographic screening for breast cancer within the regional screening program "Prevenzione Serena". Breast density is an important factor because dense breast tissue can make it more difficult to detect breast cancer on a mammogram. Dense tissue and tumors both appear white on a mammogram, which may hide abnormalities and lead to missed cancers or false-positive results. Women aged 45 to 75 years who underwent routine mammographic screening at ASL CN2 between September 2023 and May 2024 will be included. Breast density will be classified using the BI-RADS system (categories A-D), and the study will assess whether women with dense breasts (categories C and D) experience higher rates of recalls for second-level examinations such as ultrasound, MRI, etc). The study also includes an internal validation of Insight BD, an automated breast-density measurement software used at ASL CN2. The software will be evaluated using a mammography phantom (to verify technical accuracy) and by comparing its BI-RADS density classifications with readings from two radiologists (one expert and one less experienced). This will help determine whether the software can support radiologists, especially in evaluating dense breast tissue. Additional factors such as menopausal status, family history of breast cancer, and hormone therapy will also be examined to understand how they relate to breast density and screening outcomes. The study aims to quantify the frequency of false-positive recalls-cases in which additional tests are recommended but cancer is not found-because these events can increase patient anxiety and healthcare workload. Ultimately, this research seeks to provide evidence that may inform future screening guidelines and support more personalized approaches, particularly for women with dense breasts.

Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited. This project aims to improve the effectiveness and feasibility of breast cancer screening by addressing the core issues and bottlenecks in population-based breast cancer screening. We will conduct a prospective cluster-controlled screening trial in the general population, with district-based cluster grouping. The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will receive conventional screening: breast ultrasound for initial screening and mammography for secondary screening. Based on population screening practices, we will evaluate the effectiveness of AI-assisted imaging diagnostic technology in various technical aspects of actual screening and perform cost-effectiveness analyses. This study will investigate the application of AI-assisted breast imaging technology in population-based breast cancer screening, providing scientific evidence for the large-scale implementation of AI-assisted imaging technologies. Furthermore, by combining population screening practices with model simulations, we will explore multi-dimensional breast cancer screening strategies to optimize screening approaches and technologies for the Chinese population.

The goal of this clinical trial is to compare patient-centered outcomes when screening digital breast tomosynthesis (DBT) exams are interpreted with versus without a leading FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings and to assess patients' and radiologists' perspectives on AI in medicine. The main question it aims to answer is: Does an FDA-cleared AI decision-support tool for digital tomosynthesis (DBT) improve screening outcomes in real world US clinical settings? This trial will include all interpreting radiologists and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. All screening mammograms at these facilities will be randomized to either intervention (radiologist assisted by an AI decision support tool) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient screening outcomes. We are targeting 400,000 screening exams across the participating health systems in this trial.

Most physicians still use a one-size-fits-all approach to breast screening in which all women, regardless of their personal history, family history or genetics (except BRCA carriers) are recommended to have annual mammograms starting at age 40. Mammograms benefit women by detecting cancers early when they are easier to treat, but they are not perfect. Recent news stories have discussed some of the potential harms: large numbers of positive results that cause stressful recalls for additional mammograms and biopsies. With the current screening approach, half of the women who undergo annual screening for ten years will have at least one false positive biopsy. Potentially more important are cancer diagnoses for growths that might never come to clinical attention if left alone (called "overdiagnosis"). This can lead to unnecessary treatment. Even more concerning is evidence that up to 20% of breast cancers detected today may fall into the category of "overdiagnosis." The WISDOM 1.0 study compares annual screening with a risk-based breast cancer screening schedule, based upon each woman's personal risk of breast cancer. The investigators have designed the study to be inclusive of all, so that even women who might be nervous about being randomly assigned to receive a particular type of care (a procedure that is typical in clinical studies) will still be able to participate by choosing the type of care they receive. For participants in the risk-based screening arm, each woman will receive a personal risk assessment that includes her family and medical history, breast density measurement and tests for genes (mutations and variations) linked to the development of breast cancer. Women who have the highest personal risk of developing breast cancer will receive more frequent screening, while women with a lower personal risk would receive less frequent screening. No woman will be screened less than is recommended by the USPSTF breast cancer screening guidelines. If this study is successful, women will gain a realistic understanding of their personal risk of breast cancer as well as strategies to reduce their risk, and fewer women will suffer from the anxiety of false positive mammograms and unnecessary biopsies. The investigators believe this study has the potential to transform breast cancer screening in America. Starting in Spring 2023, WISDOM's design shifted to remove the randomized option, but will continue with the preference/self-selection option for participation (WISDOM 2.0). Participants will therefore continue to choose their study arm (Personalized or Annual) rather than have the option to be randomized. This study design change was made after review of the WISDOM 1.0 data by an independent monitoring committee, which indicates that personalized screening does not cause harm. WISDOM 2.0 has also lowered the eligibility to ages 30-74. Women ages 30-39 will only be offered to join the Personalized Arm.

The design builds on past studies by integrating social network analysis (SNA) and implementation science (IS) into a longitudinal randomized clinical trial (RCT). The investigator will compare the long-term effects of interventions by examining guideline-concordant initial and repeat Breast Cancer (BC) screening.

Construct a breast cancer risk prediction model based on traditional Chinese medicine four diagnostic instruments and B-ultrasound images. Ensure that the key evaluation indicators of the model reach a high level through clinical verification, so as to contribute to accurate clinical diagnosis and treatment decision-making.

The purpose of this study is to construct an auxiliary diagnostic model for breast cancer by methylation markers. The study will collect blood and tissue samples from participants with breast cancer and benign disease for whole-genome methylation sequencing. It will screen methylation markers and develop a methylation auxiliary diagnosis model to distinguish between breast cancer and non-cancer.