Optimizing Breast Cancer Screening Outcomes in Dense Breasts: Diagnostic Performance, Cost-Effectiveness and Implementation of Supplemental Modalities

Abstract

Dense breast tissue is a major challenge to screening of breast cancer since it covers about 40-50% of the breast and hides the lesions in traditional mammography. This weakness requires the efficient use of additional imaging modalities for high-risk patients. The following paper is a comparative analysis of the major supplemental imaging techniques, such as ultrasound, Magnetic Resonance Imaging (MRI), Contrast-Enhanced Spectral Mammography (CESM), Digital Breast Tomosynthesis (DBT), and AI-assisted mammography. A literature review was conducted in databases like PubMed, ScienceDirect, and Google Scholar, and peer-reviewed articles published since 2016, and those that concentrated on breast cancer screening in high-density breasts were assessed. The review is a confirmation of the role that mammography plays at its core, but indicates the improved detection of cancer with the help of supplemental ultrasound and MRI. Importantly, CESM has a similar diagnostic potential as MRI and the practical advantages of lower costs and shorter scan time. DBT enhances clear images by reducing overlap between tissues, and it effectively reduces the rate of recollection among patients. Moreover, AI-assisted mammography is one of the essential developments that will raise the detection of cancer in dense breasts and may even prevent the use of auxiliary procedures. The results strongly reflect the need of implementation of tailored and risk-based screening methods where such advanced add-on imaging technologies are combined with Artificial Intelligence, which is likely to elevate screening accuracy, clinical outcomes, affordability, and ultimately make a significant contribution to lowering the rates of mortality of breast cancer.