Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data.
Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data.
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DOI：
10.1016/j.neo.2020.10.011
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Jarrett AM;Hormuth DA 2nd;Wu C;Kazerouni AS;Ekrut DA;Virostko J;Sorace AG;DiCarlo JC;Kowalski J;Patt D;Goodgame B;Avery S;Yankeelov TE
准确预测反应，然后根据患者具体情况严格优化治疗方案的能力，将改变肿瘤学。为此，我们开发了一个实验-数学框架，将定量磁共振成像(MRI)数据整合到生物物理模型中，以预测局部晚期乳腺癌对新辅助治疗的患者特异性治疗反应。在治疗前、治疗1个周期后和第一个治疗方案完成时收集弥散加权和动态对比增强MRI数据。该模型用前两个特定于患者的核磁共振数据集进行初始化和校准，以预测第三个数据集的反应，然后与患者结果进行比较(N = 18)。模型对总细胞密度、总体积和方案结束时最长轴的预测在预期的测量精度内显著(P<；0.05)，并与测量反应强烈相关(P<；0.01)。此外，我们使用该模型在计算机中研究一系列(实用的)替代治疗计划，以实现对患者亚组中每个人的最大可能的肿瘤控制(N = 13)。该模型确定了与13名患者中的12名患者的护理标准相比，预测可实现更好的肿瘤控制的替代剂量策略(P<；0.01)。总之，一个预测性的、基于机理的数学模型已经证明了确定替代治疗方案的能力，这些替代治疗方案被预测为在临床上优于患者的治疗方案。这对临床试验设计具有重要意义，有机会改变未来的肿瘤学治疗。
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.
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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
3.3
作者：
Atuegwu, Nkiruka C.;Arlinghaus, Lori R.;Li, Xia;BrianWelch, E.;Chakravarthy, Bapsi A.;Gore, John C.;Yankeelov, Thomas E.
通讯作者：
Yankeelov, Thomas E.
DOI：
10.1098/rsta.2016.0153
发表时间：
2016-11-13
期刊：
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
影响因子：
--
作者：
Coveney PV;Dougherty ER;Highfield RR
通讯作者：
Highfield RR
影响因子：
10.6
作者：
Mi, Hongmei;Petitjean, Caroline;Ruan, Su
通讯作者：
Ruan, Su
激素受体状态，肿瘤特征和预后：乳腺癌患者的前瞻性队列。
DOI：
10.1186/bcr1639
发表时间：
2007
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Dunnwald LK;Rossing MA;Li CI
通讯作者：
Li CI