用神经网络加速脑瘤生长预测，实现个性化实时建模。
Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
- 结合降维方法与神经网络，从影像数据中快速反推肿瘤参数。
- 在合成数据上实现高精度预测，计算速度显著提升。
- 适合临床数字孪生应用，为精准治疗提供支持。
胶质母细胞瘤是成人中最具侵袭性的脑肿瘤之一，其生长模式具有患者特异性，受大脑微结构驱动。本文提出一种数学模型框架，可基于纵向神经影像数据实现实时预测与患者特异性参数识别。该框架采用扩散界面模型描述肿瘤演化，并结合本征正交分解的降维策略，利用从磁共振成像和弥散张量成像重建的患者特异性脑解剖结构生成合成数据进行训练。通过神经网络代理模型学习从肿瘤演化到模型参数的逆映射，实现显著的计算加速并保持高精度。为确保鲁棒性与可解释性，我们进行了全局与局部敏感性分析，识别出主导肿瘤动力学的关键生物物理参数，并评估逆问题解的稳定性。该研究为未来神经肿瘤学中患者特异性数字孪生的临床部署奠定了方法基础。
原文摘要 · Abstract (English)
Glioblastoma is among the most aggressive brain tumors in adults, characterized by patient-specific invasion patterns driven by the underlying brain microstructure. In this work, we present a proof-of-concept for a mathematical model of GBL growth, enabling real-time prediction and patient-specific parameter identification from longitudinal neuroimaging data. The framework exploits a diffuse-interface mathematical model to describe the tumor evolution and a reduced-order modeling strategy, relying on proper orthogonal decomposition, trained on synthetic data derived from patient-specific brain anatomies reconstructed from magnetic resonance imaging and diffusion tensor imaging. A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, achieving significant computational speed-up while preserving high accuracy. To ensure robustness and interpretability, we perform both global and local sensitivity analyses, identifying the key biophysical parameters governing tumor dynamics and assessing the stability of the inverse problem solution. These results establish a methodological foundation for future clinical deployment of patient-specific digital twins in neuro-oncology.
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