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：弥漫性胶质瘤是恶性脑肿瘤，广泛浸润大脑组织。肿瘤细胞与正常组织间的复杂相互作用，以及常遇到的治疗诱导性变化，使得胶质瘤生长建模极具挑战性。本文提出一种新颖的端到端网络，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展现肿瘤外观的高保真MRI图像。该模型基于先进的扩散概率模型和深度分割神经网络构建。我们扩展了扩散模型，将纵向多参数MRI及治疗信息作为条件输入纳入生成性扩散过程，从而可在任意给定时间点估计肿瘤生长。采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时间序列上的肿瘤分割图表示。该模型在多项任务中展现出优异性能，包括生成含肿瘤掩膜的高质量合成MRI、时间序列肿瘤分割及不确定性估计。结合治疗感知生成性MRI，具备不确定性估计的肿瘤生长预测可为临床决策提供重要参考。