比较经典与神经ODE肿瘤模型，发现通用贝塔兰菲模型更优
New tools for comparing classical and neural ODE models for tumor growth
- 用新工具TumorGrowth.jl对比经典与神经ODE肿瘤模型
- 平均6.3次测量下，通用贝塔兰菲模型表现最佳
- 数据多时，复杂模型更擅长捕捉复发反弹行为
本文介绍了一种新的计算工具 TumorGrowth.jl，用于建模肿瘤生长。该工具可对比经典教科书模型（如通用贝塔兰菲模型和戈姆佩茨模型）与较新模型，首次引入神经ODE模型进行对比。以非小细胞肺癌和膀胱癌患者接受两种不同治疗方案的荟萃研究为例，分析已有性能差异是否具有统计显著性，并评估新型复杂模型是否表现更优。在至少有4次时间-体积测量用于校准的群体中，平均约6.3次测量下，通用贝塔兰菲模型整体表现最优。然而，在测量次数较多的情况下，能捕捉复发和反弹行为的复杂模型可能更具优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。