Personalized optimization strategy for electrode array layout in TTFields of glioblastoma

• Published in: International journal for numerical methods in biomedical engineering Vol. 40; no. 10; pp. e3859 - n/a
• Main Authors: Wang, Liang, Chen, Chunxiao, Xiao, Yueyue, Gong, Rongfang, Shen, Jun, Lu, Ming
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2024; Wiley Subscription Services, Inc
• Subjects: Algorithms; Arrays; Brain Neoplasms - therapy; Cell proliferation; Customization; Effectiveness; electric field intensity; Electric fields; Electric Stimulation Therapy - instrumentation; Electric Stimulation Therapy - methods; electrode arrays; Electrodes; Glioblastoma; Glioblastoma - therapy; Glioma; Humans; improved subtraction‐average‐based optimization; Layouts; Machine learning; Optimization; Precision Medicine - methods; TTFields; Tumor cells; Tumors; TTFields; electric field intensity; improved subtraction‐average‐based optimization; electrode arrays
• ISSN: 2040-7939; 2040-7947; 2040-7947
• DOI: 10.1002/cnm.3859

Tumor treating fields (TTFields) is a novel therapeutic approach for the treatment of glioblastoma. The electric field intensity is a critical factor in the therapeutic efficacy of TTFields, as stronger electric field can more effectively impede the proliferation and survival of tumor cells. In this study, we aimed to improve the therapeutic effectiveness of TTFields by optimizing the position of electrode arrays, resulting in an increased electric field intensity at the tumor. Three representative head models of real glioblastoma patients were used as the research subjects in this study. The improved subtraction‐average‐based optimization (ISABO) algorithm based on circle chaos mapping, opposition‐based learning and golden sine strategy, was employed to optimize the positions of the four sets of electrode arrays on the scalp. The electrode positions are dynamically adjusted through iterative search to maximize the electric field intensity at the tumor. The experimental results indicate that, in comparison to the conventional layout, the positions of the electrode arrays obtained by the ISABO algorithm can achieve average electric field intensity of 1.7887, 2.0058, and 1.3497 V/cm at the tumor of three glioblastoma patients, which are 23.6%, 29.4%, and 8.5% higher than the conventional layout, respectively. This study demonstrates that optimizing the location of the TTFields electrode array using the ISABO algorithm can effectively enhance the electric field intensity and treatment coverage in the tumor area, offering a more effective approach for personalized TTFields treatment. The ISABO algorithm has been developed to optimize the positioning of electrode arrays, enhancing the efficacy of tumor treating fields (TTFields). By dynamically adjusting the positions of electrodes on the scalp, the algorithm increases the electric field intensity in the tumor region. This optimization approach improves treatment coverage, thereby enhancing therapeutic outcomes for glioblastoma patients and enabling more effective personalized TTFields therapy.