3D NUMERICAL STUDY OF TEMPERATURE PATTERNS IN A FEMALE BREAST WITH TUMOR USING A REALISTIC MULTI-LAYERED MODEL

2021

Closed access Metadata (CSL-JSON)
DOI 10.32014/2021.2518-1467.1
Data confidence 85 high
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

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.