Simulation model of tumor-treating fields_Journal of Biomedical Engineering

Authors：

QIN Liping ¹ , XIE Xu ^2,3 ,  WANG Minmin ² , MA Mingwei ² , PAN Yun ³ , CHEN Guangdi ⁴ , ZHANG Shaomin ²

1. Zhejiang Institute of Medical Device Testing, Hangzhou 310018, P. R. China;
2. Qiushi Academy for Advanced Studies, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, P. R. China;
3. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, P. R. China;
4. School of Public Health, Zhejiang University, Hangzhou 310027, P. R. China;

Corresponding author：

WANG Minmin, Email: minminwang@zju.edu.cn

Keywords：

Tumor-treating fields; Simulation model; Finite element; Glioblastoma

DOI：

10.7507/1001-5515.202306074

Video：

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Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm³. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

Citation： QIN Liping, XIE Xu, WANG Minmin, MA Mingwei, PAN Yun, CHEN Guangdi, ZHANG Shaomin. Simulation model of tumor-treating fields. Journal of Biomedical Engineering, 2024, 41(2): 360-367. doi: 10.7507/1001-5515.202306074 Copy

1.	Kirson E D, Gurvich Z, Schneiderman R, et al. Disruption of cancer cell replication by alternating electric fields. Cancer Research, 2004, 64(9): 3288-3295.
2.	Stupp R, Wong E T, Kanner A A, et al. NovoTTF-100A versus physician’ s choice chemotherapy in recurrent glioblastoma: a randomised phase III trial of a novel treatment modality. European Journal of Cancer, 2012, 48(14): 2192-2202.
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5.	Shi W, Blumenthal D T, Kebir S, et al. Global post-marketing safety surveillance of tumor treating fields (TTFields) in patients with high-grade glioma in clinical practice. Journal of Neuro-oncology, 2020, 148(3): 489-500.
6.	Gera N, Yang A, Holtzman T S, et al. Tumor treating fields perturb the localization of septins and cause aberrant mitotic exit. Plos One, 2015, 10(5): e0125269.
7.	Kissling C, Di Santo S. Tumor treating fields–behind and beyond inhibiting the cancer cell cycle. CNS Neurol Disord Drug Targets, 2020, 19(8): 599-610.
8.	Giladi M, Schneiderman R S, Voloshin T, et al. Mitotic spindle disruption by alternating electric fields leads to improper chromosome segregation and mitotic catastrophe in cancer cells. Scientific Reports, 2015, 5: 18046.
9.	Miranda P C, Mekonnen A, Salvador R, et al. Predicting the electric field distribution in the brain for the treatment of glioblastoma. Physics in Medicine & Biology, 2014, 59(15): 4137-4147.
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11.	Korshoej A R, Hansen F L, Mikic N, et al. Importance of electrode position for the distribution of tumor treating fields (TTFields) in a human brain. Identification of effective layouts through systematic analysis of array positions for multiple tumor locations. Plos One, 2018, 13(8): e0201957.
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24.	Peloso R, Tuma D T, Jain R K. Dielectric properties of solid tumors during nonnothermia and hyperthermia. IEEE Transactions on Biomedical Engineering, 1984, 31(11): 725-728.
25.	Latikka J, Kuurne T, Eskola H. Conductivity of living intracranial tissues. Physics in Medicine & Biology, 2001, 46(6): 1611-1616.
26.	Gabriel S, Lau R W, Gabriel C. The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz, Phys. Med. Biol. 1996, 41(11): 2251-2269.
27.	Gabriel C. Compilation of the dielectric properties of body tissues at RF and microwave frequencies. Brooks Air Force Base, 1996, DOI: 10.21236/ada303903.
28.	李宏宇. 胶质母细胞瘤的位置和侧别与临床特性关系的研究分析. 杭州: 浙江大学, 2018.
29.	Tan A C, Ashley D M, López G Y, et al. Management of glioblastoma: State of the art and Future Directions. CA: A Cancer Journal for Clinicians. 2020, 70(4): 299-312.
30.	Davis M E. Glioblastoma: overview of disease and treatment. Clinical Journal of Oncology Nursing. 2016, 20(5 Suppl): S2-S8.
31.	Chen T, Que Y T, Zhang Y H, et al. Using materialise's interactive medical image control system to reconstruct a model of a patient with rectal cancer and situs inversus totalis: a case report. World Journal of Clinical Cases, 2020, 8(4): 806-814.
32.	Wang Z, Aarya I, Gueorguieva M, et al. Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom. International Journal of Computer Assisted Radiology and Surgery. 2012, 7(6): 941-948.

1. Kirson E D, Gurvich Z, Schneiderman R, et al. Disruption of cancer cell replication by alternating electric fields. Cancer Research, 2004, 64(9): 3288-3295.
2. Stupp R, Wong E T, Kanner A A, et al. NovoTTF-100A versus physician’ s choice chemotherapy in recurrent glioblastoma: a randomised phase III trial of a novel treatment modality. European Journal of Cancer, 2012, 48(14): 2192-2202.
3. Kirson E D, Dbalý V, Tovaryš F, et al. Alternating electric fields arrest cell proliferation in animal tumor models and human brain tumors. Proceedings of the National Academy of Sciences, 2007, 104(24): 10152-10157.
4. Salzberg M, Kirson E, Palti Y, et al. A pilot study with very low-intensity, intermediatefrequency electric fields in patients with locally advanced and/or metastatic solid tumors. Onkologie, 2008, 31(7): 362-365.
5. Shi W, Blumenthal D T, Kebir S, et al. Global post-marketing safety surveillance of tumor treating fields (TTFields) in patients with high-grade glioma in clinical practice. Journal of Neuro-oncology, 2020, 148(3): 489-500.
6. Gera N, Yang A, Holtzman T S, et al. Tumor treating fields perturb the localization of septins and cause aberrant mitotic exit. Plos One, 2015, 10(5): e0125269.
7. Kissling C, Di Santo S. Tumor treating fields–behind and beyond inhibiting the cancer cell cycle. CNS Neurol Disord Drug Targets, 2020, 19(8): 599-610.
8. Giladi M, Schneiderman R S, Voloshin T, et al. Mitotic spindle disruption by alternating electric fields leads to improper chromosome segregation and mitotic catastrophe in cancer cells. Scientific Reports, 2015, 5: 18046.
9. Miranda P C, Mekonnen A, Salvador R, et al. Predicting the electric field distribution in the brain for the treatment of glioblastoma. Physics in Medicine & Biology, 2014, 59(15): 4137-4147.
10. Korshoej A R, Hansen F L, Thielscher A, et al. Impact of tumor position, conductivity distribution and tissue homogeneity on the distribution of tumor treating fields in a human brain: a computer modeling study. Plos One, 2017, 12(6): e0179214.
11. Korshoej A R, Hansen F L, Mikic N, et al. Importance of electrode position for the distribution of tumor treating fields (TTFields) in a human brain. Identification of effective layouts through systematic analysis of array positions for multiple tumor locations. Plos One, 2018, 13(8): e0201957.
12. Bomzon Z, Urman N, Wenger C, et al. Modelling tumor treating fields for the treatment of lung-based tumors. Annu Int Conf IEEE Eng Med Biol Soc. 2015: 6888-6891.
13. Timmons J J, Lok E, San P, et al. End-to-end workflow for finite element analysis of tumor treating fields in glioblastomas. Physics in Medicine & Biology, 2017, 62(21): 8264-8282.
14. Lok E, San P, Liang O, et al. Finite element analysis of tumor treating fields in a patient with posterior fossa glioblastoma. Journal of Neuro-oncology, 2020, 147(1): 125-133.
15. Yang X, Liu P, Xing H, et al. Skull modulated strategies to intensify tumor treating fields on brain tumor: a finite element study. Biomechanics and Modeling in Mechanobiology, 2022, 21(4): 1133-1144.
16. 杜宗伦, 曹群生, 吕著海, 等. 肿瘤电场治疗的精确电磁建模与仿真研究. 临床神经外科杂志, 2022, 19(3): 289-295.
17. Ashburner J, Friston K. Multimodal image coregistration and partitioning--a unified framework. NeuroImage, 1997, 6(3): 209-217.
18. Ashburner J, Friston K J. Voxel-based morphometry--the methods. NeuroImage, 2000, 11(6): 805-821.
19. Good C D, Johnsrude I S, Ashburner J, et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 2001, 14(1): 21-36.
20. Lombard A, Goffart N, Rogister B. Glioblastoma circulating cells: reality, trap or illusion?. Stem Cell International, 2015, 2015: 182985.
21. Lu Y, Li B, Xu J, et al. Dielectric properties of human glioma and surrounding tissue. International Journal of Hyperthermia, 1992, 8(6): 755-760.
22. Fonov V, Evans A, McKinstry R, et al. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage. 2009, 47: 39-41.
23. Fonov V, Evans AC, Botteron K, et al. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage. 2011, 54(1): 313-327.
24. Peloso R, Tuma D T, Jain R K. Dielectric properties of solid tumors during nonnothermia and hyperthermia. IEEE Transactions on Biomedical Engineering, 1984, 31(11): 725-728.
25. Latikka J, Kuurne T, Eskola H. Conductivity of living intracranial tissues. Physics in Medicine & Biology, 2001, 46(6): 1611-1616.
26. Gabriel S, Lau R W, Gabriel C. The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz, Phys. Med. Biol. 1996, 41(11): 2251-2269.
27. Gabriel C. Compilation of the dielectric properties of body tissues at RF and microwave frequencies. Brooks Air Force Base, 1996, DOI: 10.21236/ada303903.
28. 李宏宇. 胶质母细胞瘤的位置和侧别与临床特性关系的研究分析. 杭州: 浙江大学, 2018.
29. Tan A C, Ashley D M, López G Y, et al. Management of glioblastoma: State of the art and Future Directions. CA: A Cancer Journal for Clinicians. 2020, 70(4): 299-312.
30. Davis M E. Glioblastoma: overview of disease and treatment. Clinical Journal of Oncology Nursing. 2016, 20(5 Suppl): S2-S8.
31. Chen T, Que Y T, Zhang Y H, et al. Using materialise's interactive medical image control system to reconstruct a model of a patient with rectal cancer and situs inversus totalis: a case report. World Journal of Clinical Cases, 2020, 8(4): 806-814.
32. Wang Z, Aarya I, Gueorguieva M, et al. Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom. International Journal of Computer Assisted Radiology and Surgery. 2012, 7(6): 941-948.

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Simulation model of tumor-treating fields

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