发表机构
Florida Institute of Technology; Department of Mathematics and Systems Engineering（佛罗里达理工学院; 数学与系统工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对脑肿瘤进化预测和治疗计划难题，提出人工智能增强自适应数字孪生框架，集成多种模型与方法。实验表明该框架能有效提升预测准确性，降低肿瘤负荷，为患者特异性治疗优化提供统一框架，奠定向现实治疗计划转化基础。
AI 中文摘要
脑肿瘤进展呈现出空间异质性生长、患者特异性治疗反应以及与周围解剖结构的复杂相互作用，使得准确的长期预测具有挑战性。我们提出了一种用于脑肿瘤进化预测和治疗计划安排的人工智能增强自适应数字孪生（DT）框架。该框架集成了可解释的反应扩散（RD）模型、用于模型形式校正的3D残差学习模块、递归展开期间的患者特异性DT更新以及用于受限化疗和放疗计划安排的模型预测控制（MPC）。在具有120步进化的387条合成肿瘤轨迹上的实验表明，基线RD模型捕获了肿瘤位置和整体时间行为，但在长期预测中低估了异质性肿瘤负荷。混合RD-残差建模相对于RD基线在密集模拟观测下将掩膜体素平均平方误差降低了84.3%，并将骰子重叠增加了43.5%。在线DT更新与未更新的混合模型相比，进一步将平均平方误差降低了45.9%，并将骰子重叠提高了9.6%。在基于MPC的计划模拟中，更新后的DT控制器相对于终端负荷目标下的固定治疗计划将最终肿瘤负荷降低了22.4%。这些结果共同证明了一个用于患者特异性初始化、机理建模、自适应学习和受限治疗优化的统一框架。尽管使用患者数据告知的合成轨迹而非临床纵向数据进行了验证，但所提出的框架为未来向现实世界自适应治疗计划的转化奠定了基础。
英文摘要
Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term prediction challenging. We propose an AI-augmented adaptive digital twin (DT) framework for brain tumor evolution prediction and treatment scheduling. The framework integrates an interpretable reaction--diffusion (RD) model, a 3D residual learning module for model-form correction, patient-specific DT updating during recursive rollout, and model predictive control (MPC) for constrained chemotherapy and radiotherapy scheduling. Experiments on 387 synthetic tumor trajectories with 120-step evolution show that the baseline RD model captures tumor location and overall temporal behavior but underestimates heterogeneous tumor burden during long-horizon prediction. Hybrid RD--residual modeling reduces masked voxel-wise mean squared error by 84.3% and increases Dice overlap by 43.5% relative to the RD baseline under dense simulated observations. Online DT updating further reduces mean squared error by 45.9% and improves Dice overlap by 9.6% compared with the non-updated hybrid model. In MPC-based scheduling simulations, the updated DT controller reduces final tumor burden by 22.4% relative to a fixed treatment schedule under the terminal-burden objective. Together, these results demonstrate a unified framework for patient-specific initialization, mechanistic modeling, adaptive learning, and constrained treatment optimization. Although validated using patient-data-informed synthetic trajectories rather than clinical longitudinal data, the proposed framework establishes a foundation for future translation to real-world adaptive treatment planning.
