Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for breast cancer diagnosis, offering detailed insights into tumor vascular characteristics and enhancement kinetics. However, gadolinium-based contrast agents can introduce significant health risks, prompting the need for alternative enhancement solutions. To address this, we introduce 3D MedEnhancer, a latent diffusion-based framework tailored for synthesizing multi-phase DCE-MRI volumes from non-contrast T1-weighted images. Our approach employs a 3D Variational Autoencoder (VAE) and a U-shaped Diffusion Transformer (U-DiT) to capture both high-fidelity anatomical details and global spatial coherence. Extensive multi-institutional validation demonstrates notable performance gains across key breast cancer analysis tasks: for tumor segmentation, our synthetic data yield an improvement of roughly \textbf{8\%} compared to pre-contrast-only scans, nearly bridging the gap to real DCE-MRI. HER2 receptor status classification sees accuracy boosts of over 17\%, while molecular subtype classification achieves gains of over 27\%. Meanwhile, pathological complete response (PCR) prediction benefits from approximately a 5\% improvement. Collectively, these advances underscore 3D MedEnhancer’s capacity to reduce reliance on gadolinium-based contrast agents while preserving diagnostic performance that closely approximates real contrast-enhanced imaging, thus offering a safer and more efficient pathway for breast cancer evaluation.