MT-DENet: Prediction of post-therapy OCT images in diabetic macular edema by multi-temporal disease evolution network

Abstract

Predicting the therapeutic response of patients with diabetic macular edema (DME) to anti-vascular endothelial growth factor (anti-VEGF) therapy in advance is of great clinical importance, as it can support more informed treatment decisions. However, most existing works rely on a single follow-up scan, which fails to capture patient-specific factors such as individual variability and lifestyle habits, thereby introducing considerable uncertainty into predictions. Furthermore, current models rarely incorporate prior medical knowledge of disease progression, often producing anatomically implausible retinal structures. To address these issues, we propose MT-DENet, a multi-temporal disease evolution network designed to forecast post-therapy optical coherence tomography (OCT) images using pre-therapy multi-temporal OCT data. Specifically, we develop a multi-temporal cascaded graph evolution module that separately extracts features from each follow-up and sequentially evolves disease progression using a graph network and the weighted fusion of features from the current and previous time points. This design allows the model to capture patient-specific lesion evolution trends and guide subsequent prediction. In addition, we incorporate prior knowledge of anti-VEGF treatment effects into the framework and introduce a feature similarity prior constraint to reduce structural aberrations, such as abnormal retinal structures and local details. Extensive experiments on a prospective clinical DME trial dataset demonstrate that our method generates accurate and anatomically reliable OCT predictions, outperforming state-of-the-art baselines in both image quality and lesion volume estimation. The implementation is publicly available at: https://github.com/bemyself96/MT-DENet.