Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a key modality for breast cancer detection, characterization, and treatment monitoring, but its reliance on gadolinium-based contrast agents introduces safety concerns, contraindications, increased cost, and clinical workflow burden. Recent advances in deep generative modeling have demonstrated the feasibility of synthesizing post-contrast breast MRI from pre-contrast acquisitions, enabling virtual contrast-enhanced imaging as a potential alternative or complement to conventional DCE-MRI. From a technical perspective, progress in this area has been rapid but fragmented, with studies relying on heterogeneous datasets, inconsistent evaluation protocols, and limited clinical validation. This challenge addresses these limitations by providing a standardized, clinically grounded benchmark for pre-contrast to contrast-enhanced breast MRI synthesis. By jointly evaluating image fidelity, lesion-specific realism, distributional consistency, and downstream clinical utility, the challenge aims to advance trustworthy synthetic imaging methods and support future translation toward contrast-reduced or contrast-free breast MRI protocols.