It is well known that malignant tumour tissue generally has higher metabolic and blood perfusion rates than most normal tissues. The authors aim to show that the tissue temperature profile within the breast and the surface temperature profile can be quantified to develop an expert system or diagnostic tool for breast cancer detection. The surface temperature and tissue temperature profiles are analysed for a three-dimensional numerical model of a normal breast and a breast with a tumour. Tumours of different sizes are placed at various locations. In the model, the tissue temperature profile is distorted at the tumour location and was found to compare well with in vivo tests. It was also found that as the tumour was moved to deeper locations its effect on surface temperature was lower. It was observed that small tumours in deeper regions do not have a significant isolated impact on the surface. The numerical results could also capture a shift in the position of the tumour. For tumours greater than 10mm in the superficial regions and of significant size in deeper regions, it could be seen that the surface temperature distribution of the breast is directly related to the position and size of the tumour embedded in it. The feasibility of providing a diagnostic tool in conjunction with numerical modelling and high-resolution thermograms is also discussed.

Thermography is an non-invasive and a painless tool for the detection of breast cancer. However, performing and interpreting thermograms requires meticulous training. It was found that women with an abnormal thermogram are at a higher risk and have a poorer prognosis. One of the main drawbacks of the thermogram is the high incidence of false-positive results. The authors believe that the fault lies in misinterpretation of the thermogram, rather than the thermogram itself. The paper aims to show that computer simulations could be an adjunct tool to help the clinician in the interpretation. This would greatly reduce the false-positive diagnosis.

Mathematical modelling and analysis is now accepted in the engineering design on par with experimental approaches. Computer simulations enable one to perform several 'what-if' analyses cost effectively. High speed computers and low cost of memory has helped in simulating large-scale models in a relatively shorter time frame. The possibility of extending numerical modelling in the area of breast cancer detection in conjunction with medical thermography is considered in this work. Thermography enables one to see the temperature pattern and look for abnormality. In a thermogram there is no radiation risk as it only captures the infrared radiation from the skin and is totally painless. But, a thermogram is only a test of physiology, whereas a mammogram is a test of anatomy. It is hoped that a thermogram along with numerical modelling will serve as an adjunct tool. Presently mammogram is the 'gold-standard' in breast cancer detection. But the interpretation of a mammogram is largely dependent on the radiologist. Therefore, a thermogram that looks into the physiological changes in combination with numerical simulation performing 'what-if' analysis could act as an adjunct tool to mammography. The proposed framework suggested that it could reduce the occurrence of false-negative/positive cases. A numerical bioheat model of a female breast is developed and simulated. The results are compared with experimental results. The possibility of this method as an early detection tool is discussed.

Breast cancer is a common and dreadful disease in women. Regular screening helps in its early detection. At present the most common methods of screening are by self examination and mammography. The surface temperature distribution of the breast can also provide some information on the presence of tumour. This distribution has a relation to the size and location of tumour and can be seen using thermography, where the infrared radiation emitted from the surface of the breast is recorded and a thermal pattern obtained. Thermography is a non-invasive and an inexpensive tool which could be used for early detection. In order to simulate the surface temperature distribution, a two-dimensional model of female breast with and without a carcinoma is considered. The breast is modelled with varying layer thickness close to the actual shape and numerically solved using finite element analysis. Temperature profiles are obtained for a normal breast and for a malignant one by varying the tumour size, location and the blood flow rates. The results show that the surface temperature for a malignant breast is higher than that of a normal one. In addition the size and location of the tumour do have an effect on the surface temperature distribution. It can also be seen that tumour of different sizes placed at the same location would yield the same maximum temperature depending on the blood perfusion rate.

Breast cancer is a dreadful disease among women and early detection helps in achieving a cure. The mammogram is presently the standard tool for detecting breast abnormality, but its sensitivity is lower for women with dense breasts. It has been found that women with an abnormal thermogram are at a higher risk and have a poorer prognosis. However, performing and interpreting thermograms requires meticulous training. Computer simulations can be an additional tool to help the clinician in the interpretation. In this paper, a novel and flexible finite element model of a female breast is developed. Both steady state and time-dependent solutions are obtained. Steady state solutions globally match experimental thermographic results with the proper choice of blood perfusion source terms, tissue thickness and geometric scaling factor. Although the simulations may not be useful in providing a unique solution (i.e. exact size and location of the tumour owing to the complex physiological relationship between the tumour and the breast surface temperature), it would nevertheless help in the ‘analysis by elimination’. An example of this type of analysis is also presented.