Learning How, Not Just What: Explicit Evolution Modeling for Longitudinal Radiology Report Generation

Longitudinal radiology report generation (LRRG) aims to describe current findings and their temporal evolution by analyzing prior and current examinations. Existing methods largely rely on static comparisons to identify what has changed, but explicit modeling of how the disease evolves remains limited. To address this gap, we propose EvoRG, a framework for explicitly modeling disease evolution. We first aggregate visual features from both examinations using shared disease prototypes and Sinkhorn-based optimal transport. This alignment reduces interference from non-pathological spatial shifts and establishes semantically comparable disease-wise endpoints. Latent transitions between these endpoints are then parameterized as continuous geodesic trajectories on a joint visual–semantic product-sphere manifold. Logarithmic-map tangents encode evolution direction, while geodesic midpoints provide intermediate representations. Geodesic distances quantify evolution magnitude and modulate a product-of-experts module that fuses the midpoint and directed-movement descriptors. The resulting disease-specific transition tokens are used by a large language model to generate evolution-aware reports. Extensive experiments on Longitudinal-MIMIC show that EvoRG achieves state-of-the-art performance. Relative improvements over the strongest baseline for each metric are 12.5% in CIDEr and 9.6% in CheXbert F1.