A new method for optimal placement of tumor treating fields electrodes

Tumor Treating Fields (TTFields) provide a noninvasive treatment option for newly diagnosed glioblastoma. While electrode placement is considered important for treatment efficacy, current clinical planning relies on a proprietary and undisclosed software (NovoTAL). This study investigates a new computational approach for optimizing TTFields electrode placement and compares it with the current clinical standard.

We developed a computational pipeline integrating patient-specific anatomical data to optimize electrode configurations in five representative glioblastoma cases spanning diverse tumor locations and sizes. Two optimization strategies were investigated: one maximizing electric field intensity at the tumor and another increasing coverage of the surrounding brain while maintaining tumor intensity. Results were compared with electrode placements generated by NovoTAL. Additional simulations with artificial tumors assessed the effects of tumor size and location.

Optimized electrode placements increased tumor electric field intensity by 18%-34% compared with the clinical standard. Coverage-weighted optimizations achieved broader field coverage with minimal reduction in tumor intensity. Smaller and surface-adjacent tumors benefited most from optimization. Extensive randomized placement analyses demonstrated the superior performance of the optimized configurations. Artificial tumor models showed consistent improvements across a range of tumor locations and sizes.

Personalized optimization of TTFields electrode placement improved simulated electric field metrics within tumors and adjacent brain regions compared with the current clinical planning approach. These findings support the potential of patient-specific computational planning and the future development of adaptive, automated TTFields planning strategies. The clinical significance of the observed field improvements remains to be established in prospective studies.