In Silico Assessment of the Impedance Parameters of Tissue Using a 3-D Finite Element Model to Detect Breast Cancer

9 pages•Published: May 14, 2020

Thuy Nguyen Nhu Son and Quang Linh Huynh

Abstract

The early diagnosis benefits the greater efficiency of the curing of breast cancer. One of the modalities for the detection of abnormal tissue in the present day is electrical impedance myography (EIM). EIM is a noninvasive electrophysiological technique, using a high frequency, low-intensity electrical current to derive voltage at the positions of sense electrodes. A Comsol Multiphysics-based finite element model was carried out in order to build the 3D model of breast tissue for computational simulation of the mentioned technique. EIM parameters of breast tissue involving resistance, reactance, phase angle in case of malignant tissue were calculated and compared with parameters of normal tissue. The results of the study showed some remarkable effects of tumor sizes, tumor positions, and electrode positions on the EIM parameters, which can be considered for potential early detection.

Keyphrases: breast cancer, electrical impedance myography, finite element model, in silico, noninvasive

In: Tich Thien Truong, Trung Nghia Tran, Quoc Khai Le and Thanh Nha Nguyen (editors). Proceedings of International Symposium on Applied Science 2019, vol 3, pages 154-162.

Links:	https://easychair.org/publications/paper/ZZTS
	https://doi.org/10.29007/nx9d

BibTeX entry

@inproceedings{ISAS2019:Silico_Assessment_Impedance_Parameters,
  author    = {Thuy Nguyen Nhu Son and Quang Linh Huynh},
  title     = {In Silico Assessment of the Impedance Parameters of Tissue Using a 3-D Finite Element Model to Detect Breast Cancer},
  booktitle = {Proceedings of International Symposium on Applied Science 2019},
  editor    = {Tich Thien Truong and Trung Nghia Tran and Quoc Khai Le and Thanh Nha Nguyen},
  series    = {Kalpa Publications in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {/publications/paper/ZZTS},
  doi       = {10.29007/nx9d},
  pages     = {154-162},
  year      = {2020}}

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