Christenson Chase, Wu Chengyue, Hormuth David A, Stowers Casey E, LaMonica Megan, Ma Jingfei, Rauch Gaiane M, Yankeelov Thomas E
Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712.
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, 78712.
J Comput Sci. 2024 Oct;82. doi: 10.1016/j.jocs.2024.102400. Epub 2024 Aug 2.
Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption. In this work, we demonstrate that proper orthogonal decomposition, by which a low-dimensional representation of the full model is constructed, can be used to dramatically reduce the computational time required to calibrate a partial differential equation model to magnetic resonance imaging (MRI) data for rapid predictions of tumor growth and response to chemotherapy. In the proposed formulation, the reduction basis is based on each patient's own MRI data and controls the overall size of the "reduced order model". Using the full model as the reference, we validate that the reduced order mathematical model can accurately predict response in 50 triple negative breast cancer patients receiving standard of care neoadjuvant chemotherapy. The concordance correlation coefficient between the full and reduced order models was 0.986 ± 0.012 (mean ± standard deviation) for predicting changes in both tumor volume and cellularity across the entire model family, with a corresponding median local error (inter-quartile range) of 4.36% (1.22%, 15.04%). The total time to estimate parameters and to predict response dramatically improves with the reduced framework. Specifically, the reduced order model accelerates our calibration by a factor of (mean ± standard deviation) 378.4 ± 279.8 when compared to the full order model for a non-mechanically coupled model. This enormous reduction in computational time can directly help realize the practical construction of digital twins when the access to computational resources is limited.
构建用于预测肿瘤治疗反应模型的数字孪生体可能具有很高的计算需求，这为其临床应用带来了实际障碍。在这项工作中，我们证明了适当正交分解可用于显著减少将偏微分方程模型校准到磁共振成像（MRI）数据以快速预测肿瘤生长和化疗反应所需的计算时间，通过适当正交分解可以构建完整模型的低维表示。在所提出的公式中，约简基基于每个患者自身的MRI数据，并控制“降阶模型”的整体规模。以完整模型作为参考，我们验证了降阶数学模型能够准确预测50例接受标准新辅助化疗的三阴性乳腺癌患者的反应。在预测整个模型家族中肿瘤体积和细胞密度的变化时，完整模型与降阶模型之间的一致性相关系数为0.986±0.012（均值±标准差），相应的局部误差中位数（四分位间距）为4.36%（1.22%，15.04%）。使用降阶框架可以显著缩短估计参数和预测反应的总时间。具体而言，对于非机械耦合模型，与完整模型相比，降阶模型将我们的校准速度提高了（均值±标准差）378.4±279.8倍。当计算资源有限时，这种计算时间的大幅减少可以直接有助于实现数字孪生体的实际构建。
