Clinical Research Directory

Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)

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.

Bundled Cancer Screening and Genetic Services Navigation

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.

Breast Density Impact on Mammographic Screening for Breast Cancer Diagnosis

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.

AI-Enhanced Imaging in Population Breast Cancer Screening

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.

B.Brilliant Revelation Comparision Study

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

Signature Development and Validation Protocol for an Epigenetic Assay in Diagnosing Breast Cancer

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.

Breast Cancer Screening: Digital Breast Tomosynthesis Versus Digital 2D Mammography

This study is a randomized, multicenter, multivendor, controlled, diagnostic superiority trial to compare digital breast tomosynthesis plus synthesized 2D mammograms (DBT+s2D) versus standard 2D full-field digital mammography (2D-FFDM) regarding the effectiveness as screening modality.

Social Media-Based Education on Cancer Screening Awareness in Women: A Randomized Controlled Trial

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.

A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients

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.

Women Informed to Screen Depending on Measures of Risk (Wisdom Study)

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.

Digital Breast Tomosynthesis for the Dutch National Breast Cancer Screening Program

Digital breast tomosynthesis (DBT) creates a digital pseudo- three-dimensional image of the breast similar to mammography. This gives the screening radiologist more information about a possible abnormality. As a result, breast cancer can be found earlier, but more women might need to be recalled. In the STREAM study, the aim is to identify the impact of DBT on the screen-detected cancer and recall rates, and on interval and advanced cancer rates in 18,200 women after two rounds of screening. For comparison, a control group of about 86,400 women will be selected from the database of the national screening program. Women, screening radiographers, and screening radiologists will be asked whether they find this new screening technique acceptable. Furthermore, the optimal strategy for screening radiologists to read the DBT images will be identified and the cost-effectiveness of screening with DBT will be determined. The images and data will be stored in a database for future research. Expected outcome: As a result of this project, the researchers will have shown if breast cancer screening with DBT in the Netherlands should be implemented or not. It will also be demonstrated, were it to be introduced, how it should be implemented, having addressed all the remaining questions, and having found the optimal DBT workflow specifically for a high-volume population-based screening program.

MADRE (Mammograms Available Due to Research and Education)

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.

Establishment and Clinical Application of Breast Cancer Risk Prediction Model Based on Traditional Chinese Medicine Four Diagnostic Instruments and Ultrasound

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.

Application of CfDNA Methylation Detection in Auxiliary Diagnosis of Breast Cancer

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.