Paragangliomas are rare, primarily slow-growing tumors for which the underlying growth pattern is unknown. Therefore, determining the best care for a patient is hard. Currently, if no significant tumor growth is observed, treatment is often delayed, as treatment itself is not without risk. However, by doing so, the risk of (irreversible) adverse effects due to tumor growth may increase. Being able to predict the growth accurately could assist in determining whether a patient will need treatment during their lifetime and, if so, the timing of this treatment. The aim of this work is to learn the general underlying growth pattern of paragangliomas from multiple tumor growth data sets, in which each data set contains a tumor's volume over time. To do so, we propose a novel approach based on genetic programming to learn a function class, i.e., a parameterized function that can be fit anew for each tumor. We do so in a unique, multi-modal, multi-objective fashion to find multiple potentially interesting function classes in a single run. We evaluate our approach on a synthetic and a real-world data set. By analyzing the resulting function classes, we can effectively explain the general patterns in the data.

翻译：副神经节瘤是一种罕见的、主要为缓慢生长的肿瘤，其潜在生长模式尚不明确。因此，为患者确定最佳治疗方案十分困难。目前，若未观察到显著肿瘤生长，通常会推迟治疗，因为治疗本身存在风险。然而，这样做可能会增加因肿瘤生长导致（不可逆）不良后果的风险。若能准确预测肿瘤生长，将有助于判断患者一生中是否需要进行治疗，以及治疗的时机。本研究旨在从多个肿瘤生长数据集中学习副神经节瘤的一般性潜在生长模式，每个数据集包含肿瘤体积随时间的变化。为此，我们提出了一种基于遗传编程的新方法，用于学习一个函数类，即一种可针对每个肿瘤重新拟合的参数化函数。我们以独特的多模态、多目标方式进行学习，单次运行即可找到多个潜在有趣的函数类。我们在合成数据集和真实数据集上评估了该方法。通过分析得到的函数类，我们能够有效解释数据中的一般性模式。