Generative Forecasting of Longitudinal 3D-OCT Under Anti-VEGF Therapy for Diabetic Macular Edema

Background/Objectives: Forecasting the individual anatomical response to anti-vascular endothelial growth factor (anti-VEGF) therapy from optical coherence tomography (OCT) could support the management of diabetic retinal disease. Direct volumetric generation is difficult because treatment-associated change is local while most of the retina is stationary, so generative models regress to the mean and blur retinal layers.

Methods

We propose a two-stage framework that decouples baseline anatomy from longitudinal change in 3D-OCT. A Retinal Response Tokenizer compresses the latent residual between baseline and follow-up volumes into discrete action tokens via Finite Scalar Quantization (FSQ), and a conditional decoder renders the follow-up volume from baseline features and these tokens. An Action Transformer predicts the tokens from the baseline scan, the follow-up interval, and baseline covariates.