Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.
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DOI：
10.3389/frai.2023.1222612
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发表时间：
2023
影响因子：
4
通讯作者：
Willcox, Karen
中科院分区：
文献类型：
--
作者：
Chaudhuri, Anirban;Pash, Graham;Hormuth II, David A.;Lorenzo, Guillermo;Kapteyn, Michael;Wu, Chengyue;Lima, Ernesto A. B. F.;Yankeelov, Thomas E.;Willcox, Karen
关键词：
我们开发了一种方法来创建数据驱动的预测数字双胞胎，以实现最佳的风险意识临床决策。我们将该方法作为预期个性化治疗的推动者，该治疗可解释高级别胶质瘤中潜在肿瘤生物学的不确定性，其中对标准治疗（SOC）放射治疗反应的异质性导致患者结局不佳。数字孪生是通过来自人口水平的临床数据在文献中的机械模型的参数的先验分布初始化。然后使用贝叶斯模型校准来个性化数字孪生，以同化患者特定的磁共振成像数据。校准后的数字孪生模型通过求解不确定性条件下基于风险的多目标优化问题，提出最优的放射治疗方案。该解决方案导致一套患者特异性的最佳放射治疗方案，其在两个竞争的临床目标之间表现出不同水平的权衡：（i）最大化肿瘤控制（其特征在于最小化肿瘤体积生长的风险）和（ii）最小化来自放射治疗的毒性。提出的数字双胞胎框架是通过生成一个在硅片队列的100例患者与高级别胶质瘤生长和响应特性通常在文献中观察到。对于与SOC相同的总辐射剂量，个性化治疗方案导致肿瘤进展时间的中位数增加约6天。或者，对于与SOC相同的肿瘤控制水平，数字孪生提供了最佳治疗选择，与SOC总剂量60戈伊相比，辐射剂量中位降低16.7%（10戈伊）。最佳解决方案的范围还为患有侵袭性癌症的患者提供了增加剂量的选择，其中SOC不会导致足够的肿瘤控制。
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.
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影响因子：
5.2
作者：
Hormuth DA 2nd;Phillips CM;Wu C;Lima EABF;Lorenzo G;Jha PK;Jarrett AM;Oden JT;Yankeelov TE
通讯作者：
Yankeelov TE
DOI：
10.1016/s0360-3016(03)01623-7
发表时间：
2004-03-01
影响因子：
7
作者：
Floyd, NS;Woo, SY;Butler, EB
通讯作者：
Butler, EB
影响因子：
2
作者：
Hormuth DA 2nd;Weis JA;Barnes SL;Miga MI;Rericha EC;Quaranta V;Yankeelov TE
通讯作者：
Yankeelov TE
建立基于图像的分割放射治疗体内反应数学模型。
影响因子：
5.2
作者：
Hormuth DA 2nd;Jarrett AM;Davis T;Yankeelov TE
通讯作者：
Yankeelov TE
影响因子：
--
作者：
Bland, JM;Altman, DG
通讯作者：
Altman, DG