This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as “model agnostic” in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction–diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

中文翻译：

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肿瘤生长模型的选择、校准和验证

本文介绍了解决预测计算肿瘤学中一些最重要问题的一般方法，这些问题涉及开发肿瘤生长的预测模型类别。首先，开发在活组织的复杂、异质、宏观环境中进化的血管肿瘤数学模型的过程；其次，在给定相关观测数据的情况下，在这些类别中选择最合理的模型；第三，这些类别中模型的统计校准和验证，最后，预测与患者生存和各种疗法效果相关的关键兴趣量 (QOI)。这项工作最具挑战性的方面是所有这些问题通常都涉及混杂的不确定性：观测数据、模型参数、模型选择、以及预测中的目标特征。我们的方法可以被称为“模型不可知论”，因为不提倡单一模型。相反，提出了一种在考虑一系列相关生物因素的同时探索组织行为的强大混合理论表示的一般方法，这导致了许多潜在的预测模型。然后确定代表类，它们为 OPAL 的实施提供了起点，奥卡姆合理性算法 (OPAL) 使建模者能够选择最合理的模型（对于给定的数据）并确定该模型是否是用于预测的有效工具肿瘤生长和形态（体内）。所有这些方法都考虑了模型、观测数据、模型参数和目标 QOI 中的不确定性。我们通过比较一系列肿瘤生长模型来证明这些过程，包括反应扩散模型、相场模型以及具有和不具有机械变形效应的模型，用于小鼠实验中测量的神经胶质瘤生长。所提供的示例展示了实验动物中肿瘤生长的完全可接受的预测，同时展示了 OPAL 的成功实施。