Modeling of electric field distribution in tissues during electroporation.
Modeling of electric field distribution in tissues during electroporation.
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DOI：
10.1186/1475-925x-12-16
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发表时间：
2013-02-21
影响因子：
3.9
通讯作者：
Miklavcic D
中科院分区：
文献类型：
--
作者：
Corovic S;Lackovic I;Sustaric P;Sustar T;Rodic T;Miklavcic D
基于电穿孔的治疗和治疗（例如电化学疗法、用于基因治疗和 DNA 疫苗接种的基因电转移、不可逆电穿孔的组织消融和透皮药物递送）需要通过个性化治疗计划程序精确预测治疗或治疗结果。电穿孔组织内局部电场分布的数值模型已成为临床和实验环境中治疗计划程序的重要工具。最近的研究报告称，数值模型中预定义的电特性（即治疗组织的电导率和电穿孔引起的电导率增加率）的不确定性对基于电穿孔的治疗和治疗效果有很大影响。我们研究的目的是调查在模拟组织对电穿孔脉冲的响应时是否需要考虑组织电导率的增加，以及它如何影响电穿孔组织内的局部电分布。我们为单一组织（一种类型的组织，例如肝脏）和复合组织（几种类型的组织，例如皮下肿瘤）建立了 3D 数值模型。我们的计算机模拟是通过使用基于有限元方法的三种不同建模方法来执行的：逆分析、非线性参数分析和序贯分析。我们比较了线性（即组织电导率恒定）模型和非线性（即组织电导率取决于电场）模型。通过计算拟合优度测量，我们将数值模拟的结果与体内测量的结果进行比较。我们的研究结果表明，非线性模型（即组织电导率取决于电场：σ(E)）比线性模型（即组织电导率恒定）更适合实验数据。对于单一组织和复合组织都发现了这一点。我们在复合组织线性模型（即不考虑σ(E)关系的皮下肿瘤模型）中的电场分布建模结果表明，非常高的电场（高于不可逆阈值）仅集中在角质层，而目标肿瘤组织没有成功治疗。此外，假设每个组织的电导率恒定，则皮下模型中暴露于高于可逆阈值的电场的目标肿瘤组织的计算体积为零。我们的结果还表明，逆分析可以识别基线组织电导率（即非电穿孔组织的电导率）和电穿孔组织的组织电导率与电场（σ（E））。我们对电穿孔期间组织中电场分布的建模结果表明，在计划或研究基于电穿孔的治疗时，需要考虑电穿孔引起的电导率变化。我们得出的结论是，考虑到电穿孔导致的电导率增加的电场分布模型可以更准确地预测成功电穿孔的目标组织体积。我们的研究结果可以极大地促进当前基于个体化患者特异性电穿孔的治疗计划的发展。
Electroporation based therapies and treatments (e.g. electrochemotherapy, gene electrotransfer for gene therapy and DNA vaccination, tissue ablation with irreversible electroporation and transdermal drug delivery) require a precise prediction of the therapy or treatment outcome by a personalized treatment planning procedure. Numerical modeling of local electric field distribution within electroporated tissues has become an important tool in treatment planning procedure in both clinical and experimental settings. Recent studies have reported that the uncertainties in electrical properties (i.e. electric conductivity of the treated tissues and the rate of increase in electric conductivity due to electroporation) predefined in numerical models have large effect on electroporation based therapy and treatment effectiveness. The aim of our study was to investigate whether the increase in electric conductivity of tissues needs to be taken into account when modeling tissue response to the electroporation pulses and how it affects the local electric distribution within electroporated tissues. We built 3D numerical models for single tissue (one type of tissue, e.g. liver) and composite tissue (several types of tissues, e.g. subcutaneous tumor). Our computer simulations were performed by using three different modeling approaches that are based on finite element method: inverse analysis, nonlinear parametric and sequential analysis. We compared linear (i.e. tissue conductivity is constant) model and non-linear (i.e. tissue conductivity is electric field dependent) model. By calculating goodness of fit measure we compared the results of our numerical simulations to the results of in vivo measurements. The results of our study show that the nonlinear models (i.e. tissue conductivity is electric field dependent: σ(E)) fit experimental data better than linear models (i.e. tissue conductivity is constant). This was found for both single tissue and composite tissue. Our results of electric field distribution modeling in linear model of composite tissue (i.e. in the subcutaneous tumor model that do not take into account the relationship σ(E)) showed that a very high electric field (above irreversible threshold value) was concentrated only in the stratum corneum while the target tumor tissue was not successfully treated. Furthermore, the calculated volume of the target tumor tissue exposed to the electric field above reversible threshold in the subcutaneous model was zero assuming constant conductivities of each tissue. Our results also show that the inverse analysis allows for identification of both baseline tissue conductivity (i.e. conductivity of non-electroporated tissue) and tissue conductivity vs. electric field (σ(E)) of electroporated tissue. Our results of modeling of electric field distribution in tissues during electroporation show that the changes in electrical conductivity due to electroporation need to be taken into account when an electroporation based treatment is planned or investigated. We concluded that the model of electric field distribution that takes into account the increase in electric conductivity due to electroporation yields more precise prediction of successfully electroporated target tissue volume. The findings of our study can significantly contribute to the current development of individualized patient-specific electroporation based treatment planning.