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Computational Simulation and Experimental Validation of Electric Field Distribution Patterns in TTFields Therapy for Lung Cancer

Bioelectromagnetics. 2026 Jul;47(5):e70064. doi: 10.1002/bem.70064.

ABSTRACT

Tumor treating fields (TTFields) is a non-invasive therapeutic technology that disrupts mitotic division via intermediate-frequency alternating electric fields. For non-small cell lung cancer (NSCLC) at 150 kHz, complex thoracic anatomy and heterogeneous tissue properties often hinder the attainment of the therapeutic threshold (≥ 1 V/cm). To overcome this, high-fidelity Duke (male) and Ella (female) anatomical models were employed for full-wave simulations. The coordinated deployment of orthogonal transducer arrays (AP-20, LR-20, LR-13) with sex-specific tuning substantially enhanced electric-field coverage in the lower and lateral lung regions. Furthermore, modifying lung dielectric parameters by 20% demonstrated that these configurations maintain stable therapeutic coverage, exhibiting robustness against potential physiological or pathological variations. To provide an experimental foundation, in vivo murine measurements were conducted. Rather than attempting to replicate deep spatial complexities of the human body, these experiments served as a translational bridge to validate macroscopic voltage transfer efficiency and system-level losses. By introducing a physically derived correction factor (Roi) to account for voltage delivery drops, statistical analyses confirmed a high agreement between simulated and in vivo datasets, verifying the reliability of the computational framework. Regarding safety, the computed electrode-skin current density remained strictly below 31 mA/cm2, which, alongside built-in clinical hardware temperature limits, effectively mitigates the risk of thermal and stimulation-induced injuries. Ultimately, this optimized strategy provides a complementary, independent physical modality that integrates bioelectromagnetic modeling with preclinical validation, offering a reliable theoretical reference to facilitate individualized NSCLC treatment planning.

PMID:42359634 | DOI:10.1002/bem.70064

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