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.

翻译：弥漫性胶质瘤是一种通过大脑广泛扩散的恶性脑肿瘤。肿瘤细胞与正常组织之间的复杂相互作用，以及治疗过程中常见的变化，使得胶质瘤生长建模极具挑战性。本文提出了一种新颖的端到端网络，该网络能够生成未来肿瘤掩膜，并针对不同治疗方案预测肿瘤在未来任意时间点的逼真MRI影像。我们的模型基于前沿的扩散概率模型与深层分割神经网络构建。我们将扩散模型进行扩展，纳入序列多参数MRI和治疗信息作为条件输入以引导生成式扩散过程，从而能够估计任意给定时间点的肿瘤生长情况。我们使用真实世界术后纵向MRI数据训练模型，其中胶质瘤生长轨迹以随时间变化的肿瘤分割图表示。该模型在多项任务中展现出令人瞩目的性能，包括生成带肿瘤掩膜的高质量合成MRI影像、时间序列肿瘤分割以及不确定性估计。结合面向治疗生成的MRI影像，带有不确定性估计的肿瘤生长预测可为临床决策提供有用信息。