用机制模型+扩散生成，预测脑瘤生长轨迹。
Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
- 融合微分方程与引导扩散模型，建模肿瘤动态演化。
- 生成的未来MRI与真实数据空间相似度高，95%最大距离仅1.84厘米。
- 适合缺乏数据的临床研究，可输出生长方向与概率图。
预测脑瘤时空进展对神经肿瘤临床决策至关重要。我们提出一种混合机制学习框架，将数学肿瘤生长模型（常微分方程系统）与引导去噪扩散隐式模型（DDIM）结合，从前期MRI合成符合解剖结构的未来影像。该机制模型捕捉包括放疗效应在内的肿瘤时间动态，并估计未来肿瘤负荷；这些估计作为条件输入至梯度引导的DDIM，实现与预测生长及患者解剖一致的图像生成。模型在BraTS成人与儿童胶质瘤数据集上训练，评估基于院内纵向儿童弥漫性中线胶质瘤（DMG）60个轴向切片。结果表明，生成的随访影像在空间相似性指标上表现良好；同时引入肿瘤生长概率图，体现临床相关范围与方向性，95%分位数豪斯多夫距离为1.84厘米。该方法在数据有限场景下实现生物学先验引导的生成式时空预测。
原文摘要 · Abstract (English)
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.
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