Phillips C M, Lima E A B F, Wu C, Jarrett A M, Zhou Z, Elshafeey N, Ma J, Rauch G M, Yankeelov T E
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, 78712, Texas, United States of America.
Texas Advanced Computing Center, The University of Texas at Austin, 10100 Burnet Rd (R8700), Austin, 78758, Texas, United States of America.
J Comput Sci. 2023 May;69. doi: 10.1016/j.jocs.2023.102006. Epub 2023 Apr 5.
We develop a family of mathematical models to predict patient-specific response to neoadjuvant therapy in breast cancer. The models capture key features of tumor growth, therapeutic response, and tissue mechanics that are informed by diffusion weighted and dynamic contrast-enhanced magnetic resonance imaging. We then calibrate the models to synthetic and clinical data using Bayesian inference to give a description of the parameter uncertainties. Given the family of models and the calibration scheme, we perform three analyses. First, we test the identifiability of each model; that is, given synthetic data with the same level of noise as that seen in the clinical setting, are we able to accurately recover parameter values employed to generate the data? Second, we test the identifiability of the framework itself; that is, when data is generated by one model from the family, is that model selected as the best one during the calibration? Third, we apply our model family to predict patient-specific response on a cohort of 32 patients with triple negative breast cancer. For analysis 1, we show that we can recover the parameters used to generate synthetic data (with 5%, 10%, and 15% Gaussian noise - greater than that typically seen in magnetic resonance imaging in the clinical setting) with a mean error of 5.9% (+/-1.4%). For analysis 2, the model used to generate the data is selected as the best model for over 50% of the synthetic data sets, provided that the noise level in the synthetic data is less than 10%. For analysis 3, we show that the calibrated drug efficacy rate in the diffusion and proliferation mechanically coupled, drug informed, reaction diffusion model strongly correlates with patient response to therapy with an area under the curve score of 0.85 in a receiver operator characteristic analysis. Thus, our framework shows that, within the noise levels encountered in the clinical setting, a high level of rigor can be achieved for mathematical model parameterizations and selections, and this translates into high accuracy for predicting responders and non-responders to neoadjuvant therapy.
我们开发了一系列数学模型，以预测乳腺癌患者对新辅助治疗的个体特异性反应。这些模型捕捉了肿瘤生长、治疗反应和组织力学的关键特征，这些特征由扩散加权和动态对比增强磁共振成像提供信息。然后，我们使用贝叶斯推理将模型校准到合成数据和临床数据，以描述参数的不确定性。鉴于模型系列和校准方案，我们进行了三项分析。首先，我们测试每个模型的可识别性；也就是说，给定与临床环境中所见噪声水平相同的合成数据，我们是否能够准确恢复用于生成数据的参数值？其次，我们测试框架本身的可识别性；也就是说，当数据由该系列中的一个模型生成时，在校准过程中该模型是否被选为最佳模型？第三，我们将我们的模型系列应用于预测32例三阴性乳腺癌患者的个体特异性反应。对于分析1，我们表明我们能够恢复用于生成合成数据的参数（具有5%、10%和15%的高斯噪声——高于临床环境中磁共振成像通常所见的噪声），平均误差为5.9%（±1.4%）。对于分析2，如果合成数据中的噪声水平小于10%，则用于生成数据的模型被选为超过50%的合成数据集的最佳模型。对于分析3，我们表明，在扩散和增殖机械耦合、药物影响的反应扩散模型中校准的药物有效率与患者的治疗反应密切相关，在接受者操作特征分析中的曲线下面积得分为0.85。因此，我们的框架表明，在临床环境中遇到的噪声水平范围内，可以实现数学模型参数化和选择的高度严谨性，这转化为预测新辅助治疗反应者和无反应者的高精度。
