Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.
Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.
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DOI：
10.1016/j.cma.2016.08.024
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发表时间：
2017-02-01
影响因子：
7.2
通讯作者：
Yankeelov TE
中科院分区：
文献类型：
--
作者：
Weis JA;Miga MI;Yankeelov TE
使用定量医学成像数据来初始化和约束肿瘤生长的机械数学模型已经证明了预测治疗反应的令人信服的策略。更具体地说，我们已经证明了一个数据驱动的框架，用于预测乳腺癌新辅助治疗后的残留肿瘤负荷，该框架使用生物物理数学模型，结合反应扩散生长/治疗动力学和早期时间点成像数据驱动的生物力学效应。而早期的工作已经基于有限的降维（二维平面建模分析），以简化数值实现，在这项工作中，我们扩展我们的框架，一个完整的体积，三维生物物理数学建模方法，其中参数估计生成的逆问题的基础上的伴随状态方法的数值效率。在计算机性能研究中，我们显示了准确的参数估计，与地面实况相比误差小于3%。我们将该方法应用于病理完全缓解患者和残留肿瘤负荷患者的患者数据，并通过成像数据观察与肿瘤细胞结构和体积的模型预测之间的直接比较来证明技术可行性和预测潜力。与我们之前的二维建模框架的比较反映了通过包含患者特定数据的额外成像切片来增强模型对残留肿瘤负荷的预测。
The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data.
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影响因子：
3.3
作者：
Aryal, Madhava P.;Nagaraja, Tavarekere N.;Keenan, Kelly A.;Bagher-Ebadian, Hassan;Panda, Swayamprava;Brown, Stephen L.;Cabral, Glauber;Fenstermacher, Joseph D.;Ewing, James R.
通讯作者：
Ewing, James R.
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.
影响因子：
3.7
作者：
Cheng G;Tse J;Jain RK;Munn LL
通讯作者：
Munn LL
影响因子：
19.7
作者：
CHENEVERT, TL;BRUNBERG, JA;PIPE, JG
通讯作者：
PIPE, JG
影响因子：
46.9
作者：
Helmlinger, G;Netti, PA;Jain, RK
通讯作者：
Jain, RK