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
model is built upon cutting-edge diffusion probabilistic models and
deep-segmentation neural networks. We extended a diffusion model to include
sequential multi-parametric MRI and treatment information as conditioning input
to guide the generative diffusion process. This allows us to estimate tumor
growth 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 estimation. Combined with the treatment-aware generated MRIs,
the tumor growth predictions with uncertainty estimates can provide useful
information for clinical decision-making.

扩散性胶质瘤的成长建模具有挑战性，本论文提出了一种新颖的端到端网络，可生成未来肿瘤掩模和不同治疗计划下肿瘤在任何未来时间点的真实磁共振成像。该模型基于尖端的扩散概率模型和深度分割神经网络，通过扩展扩散模型以包括顺序多参数 MRI 和治疗信息作为条件输入，指导生成扩散过程，以估算给定时间点的肿瘤成长。通过使用真实的手术后纵向 MRI 数据作为训练数据，在各种任务中验证了该模型的优异性能，包括生成带有肿瘤掩模的高质量合成 MRI，时间序列肿瘤分割和不确定性估计。结合治疗感知生成的 MRI，具备不确定性估计的肿瘤成长预测能够为临床决策提供有用的信息。