Tumor Treating Fields: A Review of Computational Strategies for Thermal Safety and Personalization Treatment

Area of Science:

• Oncology
• Biophysics
• Computational Engineering

Background:

• Tumor Treating Fields (TTFields) therapy is a clinically proven cancer treatment disrupting mitosis via biophysical mechanisms.
• Optimizing TTFields treatment delivery for individual patients remains a significant challenge hindering broader application.

Purpose of the Study:

• To review advanced computational strategies for personalizing TTFields therapy.
• To address key challenges: accurate electric field dosimetry, thermal safety, and adaptive treatment planning.

Main Methods:

• Systematic analysis of state-of-the-art computational solutions for TTFields personalization.
• Examination of patient-specific electric field modeling, including segmentation and dosimetry.
• Review of coupled electro-thermal simulations for thermal safety analysis.
• Exploration of algorithmic array layout optimization, real-time monitoring, and synergistic interventions for adaptive planning.

Main Results:

• Patient-specific modeling requires high-fidelity segmentation and quantitative dosimetric criteria for accurate electric field prediction.
• Coupled electro-thermal simulations are crucial for predicting and mitigating thermal risks during TTFields therapy.
• Personalization involves optimizing array layouts, real-time monitoring, and integrating surgical interventions for adaptive treatment.

Conclusions:

• Computational engineering offers a problem-solution framework for advancing TTFields therapy.
• These strategies are paving the way for precise, safe, and adaptive TTFields treatments.
• Personalized computational approaches are essential for maximizing the efficacy of TTFields therapy.