非小细胞肺癌肿瘤治疗电场电极阵列布局优化方法研究

Research on the optimization of electrode array layout for tumor treating fields in non-small cell lung cancer treatment

为优化电极阵列排布,提高肿瘤治疗电场(tumor treating fields,TTF)强度,以更有效地抑制肿瘤增殖,本研究以群智能算法为基础,提出了电极感知自适应(electrode-perceptive adaptive,EPA)算法,旨在优化位于胸部区域的四组电极阵列的贴放位置,提高治疗时的电场强度。EPA算法通过迭代搜索,动态地调整电极阵列布局,可最大化肿瘤部位的电场强度,从而提升TTF治疗效果。本研究对应用EPA算法得到的电极阵列优化布局与常规布局进行了仿真实验对比。实验结果表明,相较于常规布局,EPA优化布局可显著提高肿瘤部位的平均电场强度。

In order to optimize the arrangement of electrode array and increase the intensity of tumor treating fields(TTF),the tumor proliferation was inhibited more effectively.An electrode-perceptive adaptive(EPA)algorithm based on swarm intelligence algorithm was proposed to optimize the four sets placement of electrode arrays in the chest area,and enhance the electric field intensity during treatment.The EPA algorithm dynamically adjusted the layout of electrode arrays through iterative search to maximize the electric field intensity at the tumor site,thus improved the effectiveness of TTF treatment.In the simulation experiments,the optimized electrode array layout obtained by EPA algorithm was compared with the conventional layout.The experimental results demonstrate a significant increase in the average electric field intensity at the tumor site with the EPA optimized layout compared with the conventional layout.