一种将多相偏微分方程肿瘤模型耦合的贝叶斯优化框架，以有效设计联合治疗方案
A Bayesian-optimization framework coupling a multiphase PDE tumor model to efficiently design combination therapy schedules
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中文总结 AI 辅助
研究如何设计联合癌症治疗方案，提出将多相偏微分方程肿瘤模拟器与贝叶斯优化框架耦合的方法，可在少量昂贵模拟中找到最优治疗方案，应用于三个临床场景，提供了可转移的设计优化方法。
中文摘要 AI 辅助
设计联合癌症治疗需要选择联合药物、相对剂量和给药时间，这对疗效和毒性平衡至关重要。基于耦合偏微分方程系统的高保真肿瘤生长机制模型虽能解决这些选择与肿瘤微环境的相互作用，但每次评估计算成本高，难以进行设计空间的暴力探索。我们提出一种贝叶斯优化框架，将多相、血管化的二维偏微分方程肿瘤模拟器视为黑箱，用高斯过程代理在少量昂贵模拟预算内找到使治疗效果最大化的方案。通过Python编排COMSOL Multiphysics求解器，形成全自动优化循环，单次约650天肿瘤演化模拟需约80小时运行时间。该框架应用于三个临床相关场景，与等效网格搜索相比，贝叶斯优化循环以少一到两个数量级的模拟次数收敛到临床合理的最优解，确定多西他赛诱导的放射增敏是三联疗法最优解的决定性因素，在考虑疗效和毒性时恢复与临床方案一致的分割方案。该框架与基础偏微分方程模型的具体细节无关，为昂贵的工程或生物模拟器的设计优化提供了可转移的方法。
英文摘要
Designing combination cancer therapies requires choosing not only which agents to combine but also their relative doses and timing decisions that critically shape the trade-off between efficacy and toxicity. High-fidelity mechanistic models of tumor growth, formulated as systems of coupled PDEs, can in principle resolve how these scheduling choices interact with the tumor microenvironment, but each evaluation is computationally expensive, rendering brute-force exploration of the design space intractable. We present a Bayesian Optimization framework that treats a multiphase, vascularized, two-dimensional PDE tumor simulator as a black box and uses a Gaussian-process surrogate to find schedules that maximize therapeutic outcomes within a small budget of expensive simulations. We orchestrate the COMSOL Multiphysics solver from Python, producing a fully automated optimization loop in which a single simulation of ~650 days of tumor evolution requires roughly 80 hours of wall time. The framework is applied to three clinically relevant scenarios: (i) a two-agent regimen (docetaxel + bevacizumab), (ii) a three-agent regimen (docetaxel + bevacizumab + radiation) under reduced and full intensity, and (iii) a single-agent dose-fractionation problem in which efficacy is balanced against healthy-tissue toxicity through a weighted multi-objective formulation. The BO loop converges to clinically plausible optima with one to two orders of magnitude fewer simulations than an equivalent grid search, identifies docetaxel-induced radiosensitization as a decisive factor in the triple-therapy optimum, and recovers a fractionation regime consistent with clinical protocols when both efficacy and toxicity are considered. The framework is agnostic to the specifics of the underlying PDE model and provides a transferable methodology for design optimization of expensive engineered or biological simulators.
