A new method for optimal placement of tumor treating fields electrodes

Area of Science:

• Oncology
• Biophysics
• Medical Physics

Background:

• Tumor Treating Fields (TTFields) offer a noninvasive treatment for newly diagnosed glioblastoma.
• Current TTFields planning uses proprietary NovoTAL software, with electrode placement crucial for efficacy.
• This study introduces and evaluates a novel computational approach for optimizing TTFields electrode placement.

Purpose of the Study:

• To develop and validate a computational pipeline for optimizing TTFields electrode placement in glioblastoma.
• To compare the efficacy of optimized electrode placements against the current clinical standard (NovoTAL).
• To assess the impact of tumor characteristics (size, location) on optimization outcomes.

Main Methods:

• A computational pipeline was developed using patient-specific anatomical data for glioblastoma cases.
• Two optimization strategies were employed: maximizing tumor electric field intensity and enhancing brain coverage.
• Simulations compared optimized placements with NovoTAL-generated placements and used artificial tumors to test robustness.

Main Results:

• Optimized placements increased tumor electric field intensity by 18%-34% compared to the clinical standard.
• Coverage-weighted optimization improved field distribution with minimal impact on tumor intensity.
• Smaller and surface-adjacent tumors showed the greatest benefit; simulations confirmed consistent improvements across various tumor models.

Conclusions:

• Personalized computational optimization of TTFields electrode placement enhances simulated electric field metrics.
• Findings suggest potential for patient-specific computational planning and automated TTFields strategies.
• Prospective studies are needed to confirm the clinical significance of these simulated improvements.