Cancer Institute of Iran, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Cancer Institute of Iran, Tehran University of Medical Sciences (TUMS), Tehran, Iran; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran.
Comput Biol Med. 2022 Jul;146:105511. doi: 10.1016/j.compbiomed.2022.105511. Epub 2022 Apr 18.
Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%.
准确模拟化疗过程中的肿瘤生长具有显著的潜力，可以降低未知副作用的风险并优化临床试验。在这项研究中，开发了一个包含血管生成和肿瘤生长的 3D 模拟模型，以确定血管内皮生长因子 (VEGF) 浓度并可视化微血管网络的形成。因此，评估了三种不同浓度的抗血管生成药物（贝伐单抗、雷珠单抗和布罗利珠单抗）的疗效。此外，还提出了肿瘤细胞增殖和内皮细胞血管生成的综合机制，以提供优化药物治疗的准确预测。通过机器学习 (ML) 技术，可以从模拟输出数据中提取附加特征，如肿瘤体积、肿瘤细胞数量和新血管的长度。这些特征被用来研究肿瘤生长的不同阶段和不同药物的疗效。结果表明，布罗利珠单抗的疗效最好，可使发芽新血管的长度减少多达 16%。在治疗后 20 天，最佳浓度为 10 mol m，有效率为 42%。此外，通过进行比较分析，确定了最佳的 ML 方法（与参考模拟的性能匹配）为强化学习，平均绝对误差 (MAE) 为 3.3%，平均准确率为 94.3%。
