The field of brain MRI synthesis and analysis is rapidly advancing, with a focus on developing innovative methods for generating high-quality synthetic MRIs and predicting brain tumor growth. Recent research has highlighted the importance of integrating anatomical priors into generative models to preserve subtle, medically relevant variations in brain structure. Additionally, mechanistic learning frameworks are being explored to combine mathematical models of tumor growth with image synthesis techniques, enabling biologically informed predictions of future tumor burden. These advances have the potential to significantly impact the field of neuro-oncology, enabling more accurate diagnosis and treatment planning. Noteworthy papers include: Integrating Anatomical Priors into a Causal Diffusion Model, which proposes a novel approach for generating anatomically plausible brain MRIs, and Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth, which introduces a hybrid framework for predicting tumor growth and generating realistic follow-up scans.