  Diffuse gliomas are malignant brain tumors that grow widespread through the
brain. The complex interactions between neoplastic cells and normal tissue, as
well as the treatment-induced changes often encountered, make glioma tumor
growth modeling challenging. In this paper, we present a novel end-to-end
network capable of generating future tumor masks and realistic MRIs of how the
tumor will look at any future time points for different treatment plans. Our
approach is based on cutting-edge diffusion probabilistic models and
deep-segmentation neural networks. We included sequential multi-parametric
magnetic resonance images (MRI) and treatment information as conditioning
inputs to guide the generative diffusion process. This allows for tumor growth
estimates at any given time point. We trained the model using real-world
postoperative longitudinal MRI data with glioma tumor growth trajectories
represented as tumor segmentation maps over time. The model has demonstrated
promising performance across a range of tasks, including the generation of
high-quality synthetic MRIs with tumor masks, time-series tumor segmentations,
and uncertainty estimates. Combined with the treatment-aware generated MRIs,
the tumor growth predictions with uncertainty estimates can provide useful
information for clinical decision-making.
