Parametric multiscale tumor models have been used nowadays as tools to understand and predict the behavior of tumor onset, development, and decrease under treatments. In order to obtain a useful model, its parameters have to be accurately estimated, often requiring numerous model evaluations. This can be computationally prohibitive for complex problems. In this work, we propose an approximate Bayesian computation approach for estimating model parameters using a low-fidelity Gaussian Process Regression metamodel. We develop an adaptive procedure to build the data-driven surrogate model by sequentially enriching the data set in the parametric space regions where the surrogate is not accurate enough. At the end of the process, we obtain good emulators of the original models over the entire parametric space at a low computational cost. We investigate the use of the proposed framework for the calibration of two tumor growth models, reaching high accuracy and computational efficiency, which may be key issues in many complex problems.

如今，参数化多尺度肿瘤模型已被用作了解和预测治疗下肿瘤发作、发展和减少行为的工具。为了获得有用的模型，必须准确估计其参数，通常需要多次模型评估。对于复杂的问题，这在计算上可能会令人望而却步。在这项工作中，我们提出了一种近似贝叶斯计算方法，用于使用低保真高斯过程回归元模型估计模型参数。我们开发了一种自适应程序，通过依次丰富数据集来构建数据驱动的代理模型代理项不够准确的参数空间区域。在该过程结束时，我们以较低的计算成本在整个参数空间上获得了原始模型的良好仿真器。我们研究了使用所提出的框架来校准两种肿瘤生长模型，达到高精度和计算效率，这可能是许多复杂问题的关键问题。