While artificial intelligence has achieved considerable maturity in lung nodule detection, research on growth prediction remains limited. Accurate growth prediction aids clinical decision-making, informing patient follow-up strategies. This paper proposes a novel nodule growth prediction network model that generates high-quality lung nodule images at specific time intervals. The model employs a two-branch structure for feature extraction. One branch, leveraging a displacement field prediction mechanism, models the shape transformation of pulmonary nodules through voxel-level future displacement estimation. The other branch, empowered by a three-dimensional U-Net, focused on learning texture changes within the nodules. A coordinate attention mechanism that emphasizes informative features within the extracted high-dimensional feature map. Subsequently, the outputs of both branches are fused and fed into the feature reconstruction module to generate the final lung nodule growth prediction image. Furthermore, a time interval coding module is introduced to incorporate the desired time interval into the network, enabling the generation of prediction images for different future time points.