Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

医学成像技术的进步现在允许以高时空分辨率从个体患者采集无创图像。预测肿瘤学的一项相对较新的工作是开发一种范式，用于在给定初始条件和适当的数学模型的情况下预测单个肿瘤的未来状态。本研究的目的是引入一种综合的多尺度计算方法来预测癌症和微血管网络的生长模式。设计了基于矩形晶格的模型，以便可以模拟不同的进化场景并预测扩散因素对肿瘤形态和大小的影响。此外，该模型允许基于预测的细胞和微血管行为模拟。就像单个细胞一样，每个代理都在模型中完全实现，并且交互部分由机器学习方法控制。这种多尺度计算模型的开发并结合了从患有乳腺癌的小鼠体内获取的体内微型计算机断层扫描 (microCT) 图像的输入信息。研究发现，随着癌细胞群的扩张和微血管网络之间的差异增加，与其他表型相比，细胞进行增殖和迁移的可能性更大。总体而言，多尺度计算模型符合模型训练期间未使用的理论预期和实验结果（microCT 图像）。