电化学疗法（ECT）和不可逆电穿孔术（IRE）是基于组织电场分布的癌症治疗方法。已知马铃薯（土豆组织）假体能模拟动物组织在电穿孔（EP）过程中发生的电导率变化。通过酶染色评估电场分布。然而，这种评估需要等待24小时，可能会减缓敏捷响应的情况。我们开发并验证了香蕉（香蕉）电导率模型，可以通过组织染色快速评估EP。我们通过阻抗谱分析、电导率变化和酶染色研究组织的频率响应。我们优化了三种常用的EP模型：适应的Gompertz模型、平滑的Heaviside模型和sigmoid或logistic函数。我们发现香蕉组织的介电参数与土豆相似（电导率为0.035 S/m，相对介电常数为4.1×104）。决定系数R2分别为99.94%（Gompertz）、99.85%（Heaviside）和99.58%（sigmoid）。sigmoid和Heaviside函数能以95%的置信度描述校准和验证电流。EP后3小时30分钟，我们观察到香蕉中的电穿孔区域。450 V/cm后酶染色显著。Musa acuminata的电导率模型适用于治疗计划、硬件开发和培训场景。香蕉假体支持3Rs实践，并可作为电穿孔研究中土豆的可靠替代品。版权所有 © 2023 Elsevier B.V. 发布。

Electrochemotherapy (ECT) and Irreversible electroporation (IRE) are cancer treatments based on electric field distribution in tissues. Solanum tuberosum (potato tissue) phantom is known to mimic changes in the electrical conductivity that occur in animal tissues during electroporation (EP). Electric field distribution is assessed through enzymatic staining. However, the 24-h wait for this assessment could slow agile response scenarios. We developed and validated the Musa acuminata (cavendish banana) conductivity model, which quickly evaluates EP by tissue staining. We investigated the frequency response of the tissue using impedance spectroscopy analysis, conductivity changes, and enzymatic staining. We optimized three usual EP models: adapted Gompertz, smoothed Heaviside, and the sigmoid or logistic function. We found dielectric parameters in banana tissue similar to those in potato (electrical conductivity of 0.035 S/m and relative permittivity of 4.1×104). The coefficients of determination R2 were 99.94% (Gompertz), 99.85% (Heaviside), and 99.58% (sigmoid). The sigmoid and Heaviside functions described the calibration and validation electric currents with 95% confidence. We observed the electroporated areas in bananas 3h30m after EP. Staining was significant after 450 V/cm. The conductivity model of Musa acuminata suits treatment planning, hardware development, and training scenarios. Banana phantom supports the 3Rs practice and is a reliable alternative for potato in EP studies.Copyright © 2023. Published by Elsevier B.V.