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. Results: On the OLIVES trial cohort (diabetic retinopathy and macular edema; 738 test pairs from five held-out eyes), our method achieved the highest structural similarity in every test eye (SSIM 0.657 vs. 0.543-0.623) and tracked the central subfield thickness (CST) change measured by a frozen regressor (Δ-r=0.88). In a five-fold cross-validation over 52 eyes, it improved PSNR over an identity copy by 1.27 dB (95% CI 0.93-1.57) and reduced the CST change error by 10.5 μm (95% CI 3.1-19.4). Conclusions: Prospective validation is required before clinical use.