Automated irreversible electroporated region prediction using deep neural network, a preliminary study for treatment planning

ABSTRACT

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.

KEYWORDS:

• Electroporation
• deep neural networks
• U-Net
• treatment planning
• irreversible electroporation

Acknowledgments

The author thanks Isfahan University of Medical Sciences for supporting this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.