TUG: Tumor-Guided Longitudinal MRI Forecasting with Trajectory Selection

Forecasting longitudinal MRI requires modeling tumor evolution while preserving anatomical realism, but these objectives are not always aligned. Image-oriented generative models may produce high-fidelity scans while underrepresenting tumor changes, whereas stronger tumor supervision may compromise image quality. We introduce TUG, a tumor-guided forecasting framework that bridges this appearance–evolution gap during training and inference. During training, we introduce an evolution-aware residual modulator that injects tumor structure, follow-up interval, and treatment information into the generative residuals. We further supervise the lesion expressed by one-step clean-image estimates and preserve future-tumor information in intermediate diffusion features. During inference, we introduce tumor-aware candidate scoring to progressively prune low-ranked trajectories and softly weight the completed predictions using terminal tumor evidence. Across three longitudinal glioma MRI cohorts, our approach consistently improves tumor-evolution fidelity while retaining competitive image quality, demonstrating complementary benefits from training and inference time.