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.

翻译：弥漫性胶质瘤是恶性脑肿瘤，在脑内广泛浸润生长。肿瘤细胞与正常组织之间的复杂相互作用，以及治疗引发的常见变化，使得胶质瘤生长建模具有挑战性。本文提出了一种新颖的端到端网络，该网络能够针对不同治疗方案，生成未来任意时间点的肿瘤掩膜以及肿瘤外观的逼真MRI图像。我们的方法基于前沿的扩散概率模型和深度分割神经网络。我们将序列化多参数磁共振图像（MRI）及治疗信息作为条件输入，以引导生成扩散过程，从而实现对任意给定时间点肿瘤生长的估计。我们使用真实世界中的术后纵向MRI数据训练模型，并将胶质瘤生长轨迹表示为随时间变化的肿瘤分割图。该模型在一系列任务中展示了良好的性能，包括生成带有肿瘤掩膜的高质量合成MRI、时间序列肿瘤分割图以及不确定性估计。结合面向治疗生成的MRI，具有不确定性估计的肿瘤生长预测可为临床决策提供有价值的信息。