Acquisition of dynamic contrast-enhanced MR imaging with gadolinium-based contrast agents at multiple time points provides valuable diagnostic information. In breast MRI, dynamics of enhancement serve as key indicators for differentiating malignant from benign tumors. However, acquiring delayed-phase images requires extended scan times and could lead to patient discomfort and increased costs. Furthermore, some protocols acquire only early-phase images, limiting the ability to capture dynamics of enhancement over time. In this study, we propose an iterative deep neural network that sequentially generates post-contrast images using prior outputs. By synthesizing delayed-phase images at multiple time points from early acquisitions, the proposed network enables the temporal prediction of enhancement. We evaluate our approach using a breast MRI dataset consisting of images acquired at six time points, including the pre-contrast phase. The results indicate that the proposed method can approximate delayed-phase images from early-phase images, suggesting its potential to support abbreviated scan protocols in dynamic contrast-enhanced MRI. Our code is available at: https://github.com/goglxych97/iterU-Net.git .