Open Access3D NUMERICAL STUDY OF TEMPERATURE PATTERNS IN A FEMALE BREAST WITH TUMOR USING A REALISTIC MULTI-LAYERED MODEL
Author(s) -
Yong Zhao,
A. Myrzhakhmet,
A. S. Mashekova,
E. Y. K. Ng,
Olzhas Mukhmetov
Publication year - 2021
Publication title -
vestnik nacionalʹnoj akademii nauk respubliki kazahstan
Language(s) - English
Resource type - Journals
eISSN - 2518-1467
pISSN - 1991-3494
DOI - 10.32014/2021.2518-1467.1
Subject(s) - breast tumor , parametric statistics , breast cancer , distribution (mathematics) , computer science , mathematics , statistics , medicine , mathematical analysis , cancer
This paper presents a three-dimensional numerical study of temperature patterns in a realistic multi-layered model of a female breast including blood perfusion. The breast surface temperature distributions are computed and analyzed with different tumor positions, sizes and different fat contents in the breast. The results are compared with experimental results for validation of the model. The paper shows that realistic breast model can accurately predict the temperature distributions inside the breast compared with traditional idealized models. The results demonstrate that all of the identifiable tumor occurrences were at the depth from 13 mm to 23 mm while none of the tumors at a depth of 29 mm were found to be detected. In respect to this, it was observed that tumors lied in the gland layer had less impact on the temperature profile of the breast. In addition, it was perceived that because of the natural deformation the breast geometry has an asymmetric surface temperature distribution in regards to symmetric tumor positions. Thus, the conducted parametric study analyzes the tumor location, size, and metabolic heat generation, and compares different temperature patterns subjected to the changes in the fat layer. Additionally, this study uses more realistic breast geometry model compared to previous studies. All this gives greater insight into the detectability of tumors with a variety of physiological conditions based on personalized patients’ data and can give useful insight to improve the accuracy of computer-aided diagnosis using similar breast models. This can provide a very useful tool in inverse thermal modelling for the accurate detection of tumors in the breast.