Prediction of intracranial electric field strength and analysis of treatment protocols in tumor electric field therapy targeting gliomas of the brain

Background and objective: Tumor Electric Field Therapy (TEFT) is a new treatment for glioblastoma cells with significant effect and few side effects. However, it is difficult to directly measure the intracranial electric field generated by TEFT, and the inability to control the electric field intensity distribution in the tumor target area also limits the clinical therapeutic effect of TEFT. It is a safe and effective way to construct an efficient and accurate prediction model of intracranial electric field intensity of TEFT by numerical simulation.

Methods: Different from the traditional methods, in this study, the brain tissue was segmented based on the MRI data of patients with retained spatial location information, and the spatial position of the brain tissue was given the corresponding electrical parameters after segmentation. Then, a single geometric model of the head profile with the transducer array is constructed, which is assembled with an electrical parameter matrix containing tissue position information. After applying boundary conditions on the transducer, the intracranial electric field intensity could be solved in the frequency domain. The effects of transducer array mode, load voltage and voltage frequency on the intracranial electric field strength were further analyzed. Finally, planning system software was developed for optimizing TEFT treatment regimens for patients.

Results: Experimental validation and comparison with existing results demonstrate the proposed method has a more efficient and pervasive modeling approach with higher computational accuracy while preserving the details of MRI brain tissue structure completely. In the optimization analysis of treatment protocols, it was found that increasing the load voltage could effectively increase the electric field intensity in the target area, while the effect of voltage frequency on the electric field intensity was very limited.

Conclusions: The results showed that adjusting the transducer array mode was the key method for making targeted treatment plans. The proposed method is capable prediction of intracranial electric field strength with high accuracy and provide guidance for the design of the TEFT therapy process. This study provides a valuable reference for the application of TEFT in clinical practice.