Development of a Method for Improving the Electric Field Distribution in Patients Undergoing Tumor-Treating Fields Therapy

Sung, Jaeyoung; Seo, Ji Hoon; Jo, Yunhui; Yoon, Myonggeun; Hwang, Sang‐Gu; Kim, Eun Ho

doi:10.3938/jkps.73.1577

Cited by 4 publications

(2 citation statements)

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“…11 An extensive search of the published literature through databases including PubMed, Google Scholar and Scopus was completed using Boolean OR keywords such as tumor treating fields computer simulations and optimizations, implantable electrotherapeutic devices, deep brain stimulation, electroporation, electric field optimization, simulationbased optimization, and non-convex optimization. There are publications on optimizing electric field for treatment of tumors from external devices, [7][8][9][13][14][15][16] for deep brain stimulation with multiple contacts to steer the field to treat the intended millimeter sized target, 17 and for electroporation with multiple electrodes to cover tumor volumes with large field magnitude. 18,19 The present study is the first of its kind to extend and optimize the distribution of therapeutic-range IMT fields across tumor volumes using multiple implanted electrodes rather than a single stimulation source.…”

Section: Introductionmentioning

confidence: 99%

“…Based on experience from external delivery of tumor treating fields, 7–9,13–16 deep brain stimulation optimizations, 17 and irreversible electroporation optimizations, 18,19 the incorporation of simulations and electric field optimizations ensure the desired field is being delivered to the tumor volume. The creation of methods to robustly optimize the electric field delivered to a tumor volume is a necessary step in the development of multi‐electrode IMT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of multi‐electrode implant configurations and programming for the delivery of non‐ablative electric fields in intratumoral modulation therapy

et al. 2020

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Purpose Application of low intensity electric fields to interfere with tumor growth is being increasingly recognized as a promising new cancer treatment modality. Intratumoral modulation therapy (IMT) is a developing technology that uses multiple electrodes implanted within or adjacent tumor regions to deliver electric fields to treat cancer. In this study, the determination of optimal IMT parameters was cast as a mathematical optimization problem, and electrode configurations, programming, optimization, and maximum treatable tumor size were evaluated in the simplest and easiest to understand spherical tumor model. The establishment of electrode placement and programming rules to maximize electric field tumor coverage designed specifically for IMT is the first step in developing an effective IMT treatment planning system. Methods Finite element method electric field computer simulations for tumor models with 2 to 7 implanted electrodes were performed to quantify the electric field over time with various parameters, including number of electrodes (2 to 7), number of contacts per electrode (1 to 3), location within tumor volume, and input waveform with relative phase shift between 0 and 2π radians. Homogeneous tissue specific conductivity and dielectric values were assigned to the spherical tumor and surrounding tissue volume. In order to achieve the goal of covering the tumor volume with a uniform threshold of 1 V/cm electric field, a custom least square objective function was used to maximize the tumor volume covered by 1 V/cm time averaged field, while maximizing the electric field in voxels receiving less than this threshold. An additional term in the objective function was investigated with a weighted tissue sparing term, to minimize the field to surrounding tissues. The positions of the electrodes were also optimized to maximize target coverage with the fewest number of electrodes. The complexity of this optimization problem including its non‐convexity, the presence of many local minima, and the computational load associated with these stochastic based optimizations led to the use of a custom pattern search algorithm. Optimization parameters were bounded between 0 and 2π radians for phase shift, and anywhere within the tumor volume for location. The robustness of the pattern search method was then evaluated with 50 random initial parameter values. Results The optimization algorithm was successfully implemented, and for 2 to 4 electrodes, equally spaced relative phase shifts and electrodes placed equidistant from each other was optimal. For 5 electrodes, up to 2.5 cm diameter tumors with 2.0 V, and 4.1 cm with 4.0 V could be treated with the optimal configuration of a centrally placed electrode and 4 surrounding electrodes. The use of 7 electrodes allow for 3.4 cm diameter coverage at 2.0 V and 5.5 cm at 4.0 V. The evaluation of the optimization method using 50 random initial parameter values found the method to be robust in finding the optimal solution. Conclusions This study has established a robust optimiza...

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