The paper “DeformMLP: Effective Deformation Prediction for Breast Cancer Using Graph Topology-Assisted MLPs” has been accepted for the Digital Twin for Healthcare (DT4H) workshop at MICCAI 2025. This work introduces DeformMLP, a novel approach that leverages node features generated from graph propagation to predict breast cancer deformation. The method demonstrates that we can achieve high efficiency while exploiting the graph structure without the need for graph neural networks (GNNs). Thanks to my co-authors for their contributions, as well as the reviewers for their valuable feedback.