Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.
Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.
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建立基于图像的分割放射治疗体内反应数学模型。
DOI：
10.3390/cancers13081765
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发表时间：
2021-04-07
期刊：
影响因子：
5.2
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Hormuth DA 2nd;Jarrett AM;Davis T;Yankeelov TE
使用医学成像数据和计算模型，我们开发了一个建模框架，为个体肿瘤的分次放射治疗提供个性化的治疗反应预测。我们在脑癌动物模型中评估了这种方法，并预测了肿瘤细胞和血管的变化。分次放射治疗是许多恶性肿瘤治疗的核心，包括高级别胶质瘤，其中由于其高度侵入性的性质，完全手术切除通常是不切实际的。预测分次放疗反应的方法的发展可以提供优化或调整放疗治疗计划的能力。为此，我们已经开发了一个家庭的18个生物为基础的数学模型，描述了肿瘤和血管的反应，分次放射治疗。重要的是，这些模型可以通过定量成像测量针对个体肿瘤进行个性化。为了评价该模型家族，在用分次放射治疗（剂量为2戈伊/天或4戈伊/天，长达10天）治疗之前、期间和之后，用磁共振成像（MRI）对具有U-87胶质母细胞瘤的大鼠（n = 7）进行成像。分别由扩散加权MRI和动态对比增强MRI提供的肿瘤和血液体积分数的估计值用于校准肿瘤特异性模型参数。赤池信息标准被用来选择最简约的模式，并确定一个集合平均模式，并在全球和地方一级的预测结果进行了评估。在全球水平上，所选模型的预测导致肿瘤体积估计的误差小于16.2%。在局部（体素）水平，所有动物所有预测时间点的Pearson相关系数中位数范围为0.57至0.87。虽然总体平均预测导致所选模型的肿瘤体积预测误差增加（范围从4.0%到1063%），但它增加了三只动物的体素相关性（大于12.3%）。本研究证明了通过分次放疗期间收集的系列定量MRI数据校准反应模型以预测治疗结束时的反应的可行性。
Using medical imaging data and computational models, we develop a modeling framework to provide personalized treatment response forecasts to fractionated radiation therapy for individual tumors. We evaluate this approach in an animal model of brain cancer and forecast changes in tumor cellularity and vasculature. Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.
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影响因子：
3.9
作者：
Budia, I.;Alvarez-Arenas, A.;Belmonte-Beitia, J.
通讯作者：
Belmonte-Beitia, J.
影响因子：
3.7
作者：
Corwin D;Holdsworth C;Rockne RC;Trister AD;Mrugala MM;Rockhill JK;Stewart RD;Phillips M;Swanson KR
通讯作者：
Swanson KR
DOI：
10.1098/rsif.2017.0681
发表时间：
2018-01
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Brüningk S;Powathil G;Ziegenhein P;Ijaz J;Rivens I;Nill S;Chaplain M;Oelfke U;Ter Haar G
通讯作者：
Ter Haar G
影响因子：
4.7
作者：
Jacobs VL;Valdes PA;Hickey WF;De Leo JA
通讯作者：
De Leo JA
DOI：
10.1016/j.neo.2020.10.011
发表时间：
2020-12
期刊：
Neoplasia (New York, N.Y.)
影响因子：
--
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
通讯作者：
Yankeelov TE