The Correlation between Morpho-Dynamic Contrast-Enhanced Mammography (CEM) Features and Prognostic Factors in Breast Cancer: A Single-Center Retrospective Analysis

Simple Summary Breast cancer remains a significant global health concern, demanding effective detection methods. This study explores Contrast-Enhanced Mammography (CEM), an alternative to digital mammography, for its potential in correlating with breast cancer prognostic factors. Analyzing 182 lesions in 114 women, the research identifies associations between CEM features (such as spiculated margins and irregular shape) and key prognostic factors, including tumor grade and molecular markers. Notable findings suggest that certain CEM characteristics may predict aspects of breast cancer behavior, providing valuable insights for personalized treatment decisions. While acknowledging study limitations, the results highlight the promise of CEM in contributing to our understanding of breast cancer biology and guiding clinical approaches. Further research is essential to validate and expand upon these findings.Abstract Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM's correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM's potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM's role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM's clinical utility in breast cancer assessment.