Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Electromagn Biol Med. 2022 Oct 2;41(4):379-388. doi: 10.1080/15368378.2022.2114493. Epub 2022 Aug 22.
The primary purpose of cancer treatment with irreversible electroporation (IRE) is to maximize tumor damage and minimize surrounding healthy tissue damage. Finite element analysis is one of the popular ways to calculate electric field and cell kill probability in IRE. However, this method also has limitations. This paper will focus on using a deep neural network (DNN) in IRE to predict irreversible electroporated regions for treatment planning purposes. COMSOL Multiphysics was used to simulate the IRE. The electric conductivity change during IRE was considered to create accurate data sets of electric field distribution and cell kill probability distributions. We used eight pulses with a pulse width of 100 μs, frequency of 1 Hz, and different voltages. To create masks for DNN training, a 90% cell kill probability contour was used. After data set creation, U-Net architecture was trained to predict irreversible electroporated regions. In this study, the average U-Net DICE coefficient on test data was 0.96. Also, the average accuracy of U-Net for predicting irreversible electroporated regions was 0.97. As far as we are aware, this is the first time that U-Net was used to predict an irreversible electroporated region in IRE. The present study provides significant evidence for U-Net's use for predicting an irreversible electroporated region in treatment planning.
不可逆电穿孔 (IRE) 治疗癌症的主要目的是最大限度地提高肿瘤损伤，同时最小化周围健康组织损伤。有限元分析是计算 IRE 中电场和细胞杀伤概率的一种流行方法。然而，这种方法也存在局限性。本文将重点介绍在 IRE 中使用深度神经网络 (DNN) 来预测治疗计划中的不可逆电穿孔区域。使用 COMSOL Multiphysics 模拟 IRE。考虑了 IRE 过程中的电导率变化，以创建电场分布和细胞杀伤概率分布的准确数据集。我们使用了八个宽度为 100 μs、频率为 1 Hz、不同电压的脉冲。为了创建 DNN 训练的掩模，使用了 90%细胞杀伤概率轮廓。在创建数据集后，使用 U-Net 架构对不可逆电穿孔区域进行预测。在这项研究中，U-Net 在测试数据上的平均 DICE 系数为 0.96。此外，U-Net 预测不可逆电穿孔区域的平均准确率为 0.97。据我们所知，这是首次使用 U-Net 预测 IRE 中的不可逆电穿孔区域。本研究为 U-Net 在治疗计划中预测不可逆电穿孔区域提供了重要证据。
