A new computational tool TumorGrowth$.$jl for modeling tumor growth is introduced. The tool allows the comparison of standard textbook models, such as General Bertalanffy and Gompertz, with some newer models, including, for the first time, neural ODE models. As an application, we revisit a human meta-study of non-small cell lung cancer and bladder cancer lesions, in patients undergoing two different treatment options, to determine if previously reported performance differences are statistically significant, and if newer, more complex models perform any better. In a population of examples with at least four time-volume measurements available for calibration, and an average of about 6.3, our main conclusion is that the General Bertalanffy model has superior performance, on average. However, where more measurements are available, we argue that more complex models, capable of capturing rebound and relapse behavior, may be better choices.

翻译：本文介绍了一种用于肿瘤生长建模的新型计算工具TumorGrowth$.$jl。该工具能够将标准教科书模型（如广义Bertalanffy模型和Gompertz模型）与一些新型模型进行比较，其中首次纳入了神经ODE模型。作为应用案例，我们重新审视了一项针对接受两种不同治疗方案患者的非小细胞肺癌和膀胱癌病灶的人类荟萃研究，以确定先前报道的性能差异是否具有统计学显著性，以及更新、更复杂的模型是否表现更优。在至少具备四个时间-体积测量值用于校准（平均约6.3个测量值）的样本群体中，我们的主要结论是：广义Bertalanffy模型平均具有更优的性能。然而，在可获得更多测量数据的情况下，我们认为能够捕捉肿瘤反弹和复发行为的更复杂模型可能是更好的选择。