Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
This work addresses the challenge of predicting brain tumor progression for clinical decision-making in neuro-oncology, though it is incremental as it builds on existing diffusion models and mechanistic approaches.
The authors tackled the problem of predicting spatio-temporal brain tumor growth by developing a hybrid framework that combines a mechanistic model with a guided diffusion model to synthesize future MRIs from prior scans, achieving realistic results as shown by spatial similarity metrics and 95th percentile Hausdorff Distance.
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.