Mammography

This project aims to advance methodological and theoretical approaches for developing, selecting, refining, and piloting a multilevel de-implementation strategy to reduce the overuse of screening mammography in women aged ≥75 years. Informed by an innovative participatory, stakeholder-driven innovation tournament and a discrete choice experiment, the research team identified, prioritized, and tailored a multilevel de-implementation strategy. The research team will conduct a cluster randomized controlled trial (at the provider level) to test the impact of the provider- and patient-level components of the multilevel strategy on screening mammography use, and secondarily, on provider referrals/orders for screening mammography. The organizational level components of the multilevel strategy will be implemented among all participants, not via random assignment.

To evaluate the effectiveness of MammoGRIP® in improving mammographic image quality and minimizing repeated breast positioning compared to standard practice without MammoGRIP®.

Contrast-Enhanced Mammography (CEM) is a functional breast imaging technology. Like magnetic resonance imaging (MRI), CEM neovascularization enhancement can improve cancer detection rates, while lack of enhancement may lead to benign determination. The advantages of CEM are dramatically apparent in women with dense breasts. The purpose of this study was to investigate the additional diagnostic value of CEM added to the standard practice of mammography plus ultrasonography(US) in patients with suspicious breast lesions. This study was a single-center prospective-retrospective combined observational study. In the prospective part, women with suspected breast abnormalities undergo prospective CEM evaluation, including acquisition of low energy (LE) images and recombined images (RI) showing enhancement, concurrently with breast US. The retrospective part selected the mammography and breast US data of female patients with suspected breast abnormalities who had previous baseline matching.

Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.

The purpose of this study is to evaluate how well women may adopt a Contrast-enhanced Spectral Mammography (CESM) as their yearly breast screening test compared to the standard 2-D or 3-D mammogram.