Automated Standardization of Breast Density and BPE in CEM:A Deep Learning Framework Enhancing Radiological Assessment

The assessment of breast density and background parenchymal enhancement (BPE) in contrast-enhanced mammography (CEM) remains challenged by substantial interobserver variability (κ=0.4-0.6). This study demonstrates how advanced computational methods can enhance diagnostic standardization while preserving radiologists' central role in decision-making. Analyzing 213 CEM cases, we ipotize to develop a system that improves inter-reader agreement by 40% (κ=0.82) and reduces prediction errors by 26%, with particular effectiveness in dense breasts (BI-RADS C/D categories). The findings highlight how AI-radiologist collaboration can optimize diagnostic accuracy without replacing clinical judgment, providing a more reliable approach especially for complex cases where interpretive variability most impacts patient management.