Thermal analysis of a three-dimensional breast model with embedded tumour using the transmission line matrix (TLM) method

Numerical Analysis of Temperature Distribution Profiles of Breast Tissues with Cyst and Tumor of Different sizes and Locations

Breast cancer causes more deaths among all types of cancers. Efforts have been put to study the change in temperature distribution profile of the breast in presence of an abnormality. By applying Pennes's bio-heat equation, a 2D finite element model is developed for the heat transfer mechanism. Surface temperature gradients due to the presence of abnormalities at various depths and sizes are analyzed. The results show that the presence of a cyst decreases the temperature whereas the occurrence of tumor increases the temperature inside the breast. It is observed that abnormal tissue having a radius less than 1.5cm and depth greater than 5cm, has a negligible effect on the surface temperature profile. The highest change in surface temperature is observed when a cyst or tumor is larger and present near the skin. The simulation results help in the better interpretation of the thermal images and calibration of infrared camera. This study could be helpful in the early diagnosis of breast cancer.

Review of Thermal and Physiological Properties of Human Breast Tissue

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes' bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies.

High-resolution pH imaging using ratiometric chemical exchange saturation transfer combined with biosensor imaging of redundant deviation in shifts featuring paramagnetic DOTA-tetraglycinate agents

Chemical exchange saturation transfer (CEST) and biosensor imaging of redundant deviation in shifts (BIRDS) methods differ respectively by detecting exchangeable and nonexchangeable proton signals by magnetic resonance. Because CEST contrast depends on both temperature and pH, simultaneous CEST and BIRDS imaging can be employed to separate these contributions. Here, we test if high-resolution pH imaging in vivo is possible with ratiometric CEST calibrated for temperature variations measured by BIRDS. Thulium- and europium-based DOTA-tetraglycinate agents, TmDOTA-(gly)₄^- and EuDOTA-(gly)₄^- , were used for high-resolution pH mapping in vitro and in vivo, using BIRDS for temperature adjustments needed for a more accurate ratiometric CEST approach. Although neither agent showed pH dependence with BIRDS in vitro in the pH range 6 to 8, each one's temperature sensitivity was enhanced when mixed because of increased redundancy. By contrast, the CEST signal of each agent was affected by the presence of the other agent in vitro. However, pH could be measured more accurately when temperature from BIRDS was detected. These in vitro calibrations with TmDOTA-(gly)₄^- and EuDOTA-(gly)₄^- enabled high-resolution pH imaging of glioblastoma in rat brains. It was concluded that temperature mapping with BIRDS can calibrate the ratiometric CEST signal from a cocktail of TmDOTA-(gly)₄^- and EuDOTA-(gly)₄^- agents to provide temperature-independent absolute pH imaging in vivo.

Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Breast cancer is one of the deadliest diseases in women and its incidence is growing at an alarming rate. However, early detection of this disease can be life-saving. The rapid development of deep learning techniques has generated a great deal of interest in the medical imaging field. Researchers around the world are working on developing breast cancer detection methods using medical imaging. In the present work, we have proposed a two-stage model for breast cancer detection using thermographic images. Firstly, features are extracted from images using a deep learning model, called VGG16. To select the optimal subset of features, we use a meta-heuristic algorithm called the Dragonfly Algorithm (DA) in the second step. To improve the performance of the DA, a memory-based version of DA is proposed using the Grunwald-Letnikov (GL) method. The proposed two-stage framework has been evaluated on a publicly available standard dataset called DMR-IR. The proposed model efficiently filters out non-essential features and had 100% diagnostic accuracy on the standard dataset, with 82% fewer features compared to the VGG16 model.

Theoretical Analysis for Using Pulsed Heating Power in Magnetic Hyperthermia Therapy of Breast Cancer

In magnetic hyperthermia, magnetic nanoparticles (MNPs) are used to generate heat in an alternating magnetic field to destroy cancerous cells. This field can be continuous or pulsed. Although a large amount of research has been devoted to studying the efficiency and side effects of continuous fields, little attention has been paid to the use of pulsed fields. In this simulation study, Fourier's law and COMSOL software have been utilized to identify the heating power necessary for treating breast cancer under blood flow and metabolism to obtain the optimized condition among the pulsed powers for thermal ablation. The results showed that for small source diameters (not larger than 4 mm), pulsed powers with high duties were more effective than continuous power. Although by increasing the source domain the fraction of damage caused by continuous power reached the damage caused by the pulsed powers, it affected the healthy tissues more (at least two times greater) than the pulsed powers. Pulsed powers with high duty (0.8 and 0.9) showed the optimized condition and the results have been explained based on the Arrhenius equation. Utilizing the pulsed powers for breast cancer treatment can potentially be an efficient approach for treating breast tumors due to requiring lower heating power and minimizing side effects to the healthy tissues.

Pre-operative Assessment of Ablation Margins for Variable Blood Perfusion Metrics in a Magnetic Resonance Imaging Based Complex Breast Tumour Anatomy: Simulation Paradigms in Thermal Therapies

BACKGROUND AND OBJECTIVES

Image-guided medical interventions facilitates precise visualization at treatment site. The conformal prediction for sparing healthy tissue fringes precisely in the vicinity of irregular tumour anatomy remains clinically challenging. Pre-clinical image-based computational modelling is imperative as it helps in enhancement of treatment quality, augmenting clinical-decision making, while planning, targeting, controlling, monitoring and assessing treatment response with an effective risk assessment before the onset of treatment in clinical settings. In this study, the influence of heat deposition rate (SAR), exposure duration, and variable blood perfusion metrics for a patient-specific breast tumour is quantified considering the tumour margins thereby suggesting need of geometrically accurate models.

METHODS

A three-dimensional realistic model mimicking dimensions of a female breast, comprising ~1.7 cm irregular tumour, was generated from patient specific two-dimensional DICOM format MRI images through image segmentation tools MIMICS 19.0® and 3-Matic 11.0® which is finally exported to COMSOL Multiphysics 5.2® as a volumetric mesh for finite element analysis. The Pennes bioheat transfer model and Arrhenius thermal damage model of cell-death are integrated to simulate a coupled biophysics problem. A comparative blood perfusion analysis is done to evaluate the response of tumour during heating considering thermal damage extent, including the tumour margins while sparing critical adjoining healthy tissues.

RESULTS

The evaluated thermal damage zones for 1 mm, 2 mm and 3 mm fringe heating region (beyond tumour boundary) reveals 0.09%, 0.21% and 0.34% thermal damage to the healthy tissue (which is <1%) and thus successful necrosis of the tumour. The iterative computational experiments suggests treatment margins < 5 mm are sufficient enough as heating beyond 3 mm fringe layer leads to higher damage surrounding the tumour approximately 1.5 times the tumour volume. Further, the heat-dosage requirements are 22% more for highly perfused tumour as compared to moderately perfused tumour with an approximate double time to ablate the whole tumour volume.

CONCLUSIONS

Depending on the blood perfusion characteristics of a tumour, it is a trade-off between heat-dosage (SAR) and exposure/treatment duration to get desired thermal damage including the irregular tumour boundaries while taking into account, the margin of healthy tissue. The suggested patient-specific integrated multiphysics-model based on MRI-Images may be implemented for pre-treatment planning based on the tumour blood perfusion to evaluate the thermal ablation zone dimensions clinically and thereby avoiding the damage of off-target tissues. Thus, risks involving underestimation or overestimation of thermal coagulation zones may be minimised while preserving the surrounding normal breast parenchyma.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Determining the thermal characteristics of breast cancer based on high-resolution infrared imaging, 3D breast scans, and magnetic resonance imaging

For over the three decades, various researchers have aimed to construct a thermal (or bioheat) model of breast cancer, but these models have mostly lacked clinical data. The present study developed a computational thermal model of breast cancer based on high-resolution infrared (IR) images, real three-dimensional (3D) breast surface geometries, and internal tumor definition of a female subject histologically diagnosed with breast cancer. A state-of-the-art IR camera recorded IR images of the subject’s breasts, a 3D scanner recorded surface geometries, and standard diagnostic imaging procedures provided tumor sizes and spatial locations within the breast. The study estimated the thermal characteristics of the subject’s triple negative breast cancer by calibrating the model to the subject’s clinical data. Constrained by empirical blood perfusion rates, metabolic heat generation rates reached as high as 2.0E04 W/m³ for normal breast tissue and ranged between 1.0E05–1.2E06 W/m³ for cancerous breast tissue. Results were specific to the subject’s unique breast cancer molecular subtype, stage, and lesion size and may be applicable to similar aggressive cases. Prior modeling efforts are briefly surveyed, clinical data collected are presented, and finally thermal modeling results are presented and discussed.

Reporting of thermography parameters in biology: a systematic review of thermal imaging literature

Infrared (IR) thermography, where temperature measurements are made with IR cameras, has proven to be a very useful and widely used tool in biological science. Several thermography parameters are critical to the proper operation of thermal cameras and the accuracy of measurements, and these must usually be provided to the camera. Failure to account for these parameters may lead to less accurate measurements. Furthermore, the failure to provide information of parameter choices in reports may compromise appraisal of accuracy and replicate studies. In this review, we investigate how well biologists report thermography parameters. This is done through a systematic review of biological thermography literature that included articles published between years 2007 and 2017. We found that in primary biological thermography papers, which make some kind of quantitative temperature measurement, 48% fail to report values used for emissivity (an object's capacity to emit thermal radiation relative to a black body radiator), which is the minimum level of reporting that should take place. This finding highlights the need for life scientists to take into account and report key parameter information when carrying out thermography, in the future.

Breast Cancer Estimate Modeling via PDE Thermal Analysis Algorithms

The significance of this study lies in the importance of (1) nondestructive testing in defect studies and (2) securing the reliability of breast cancer prediction through thermal analysis in nondestructive testing. Most nondestructive tests have negative effects on the human body. Moreover, the precision and accuracy of such tests are poor. This study analyzes these drawbacks and increases the reliability of such methods. A theoretical model was constructed, by which simulated inner breast tissue was observed in a nondestructive way through thermal analysis, and the presence and extent of simulated breast cancer were estimated based on the thermal observations. Herein, we studied the medical diagnosis of breast cancer by creating a theoretical environment that simulated breast cancer in a real-world setting; the model used two-dimensional modeling and partial differential equation (PDE) thermal analysis. Our theoretical analysis, based on partial differential equations, allowed us to demonstrate that non-wounding defect detection is possible and, in many ways, preferable. The main contribution of this paper lies in studying long-term estimates. In addition, the model in this study can be extended to predict breast cancer through pure heat and can also be used for various other cancer and tumor analyses in the human body.

Triangular node for Transmission-Line Modeling (TLM) applied to bio-heat transfer

Transmission-Line Modeling (TLM) is a numerical method used to solve complex and time-domain bio-heat transfer problems. In TLM, rectangles are used to discretize two-dimensional problems. The drawback in using rectangular shapes is that instead of refining only the domain of interest, a large additional domain will also be refined in the x and y axes, which results in increased computational time and memory space. In this paper, we developed a triangular node for TLM applied to bio-heat transfer that does not have the drawback associated with the rectangular nodes. The model includes heat source, blood perfusion (advection), boundary conditions and initial conditions. The boundary conditions could be adiabatic, temperature, heat flux, or convection. A matrix equation for TLM, which simplifies the solution of time-domain problems or solves steady-state problems, was also developed. The predicted results were compared against results obtained from the solution of a simplified two-dimensional problem, and they agreed within 1% for a mesh length of triangular faces of 59µm±9µm (mean±standard deviation) and a time step of 1ms.

Monitoring tumor state from thermal images in animal and human models

PURPOSE

Thermography is a potentially useful method for tumor progress monitoring since it is noninvasive, nonradiative, low-cost, and rapid. Perfusion and metabolism are dominant factors for determining tumor temperature difference and are also correlated to the tumor's growth rate. Therefore, estimating them from the tumor thermal image can be a very useful tumor monitoring method, since thermal changes occur before physical changes. The goal of this work was to study the effect of tumor state on the thermal image in different tumor types, using simulations and measurements.

METHODS

Simulated tumor models, representing flat and extruding tumors, typical to transplantable and natural tumors, respectively, were simulated and the effects of tumor metabolism and perfusion on the temperature difference were analyzed. Data regarding tumor size and measured temperature differences were obtained from the literature, discussing five types of transplantable tumors in mice and rats. The growth rates of all tumors were calculated by fitting tumor size measurements to a tumor growth model and were used as an indicator to tumor aggressiveness. Tumor temperature difference was calculated by taking the effect of its extruding shape into account, according to a previously published method. Tumor state was estimated from the normalized temperature differences using simulations and compared to the calculated aggressiveness rates. Computational models of human breast cancers, both in round and flat breast models, were recreated using a finite-element-method heat transfer simulation. Tumor size and state were simulated according to the results obtained from the animal tumor analysis, representing two different tumor aggressiveness levels. The calculated temperature difference as a function of tumor size was calculated for each test case.

RESULTS

Perfusion was shown to be highly dominant in determining the tumor's temperature difference. Since both metabolism and perfusion were shown to have a linear effect on the temperature difference, a conversion value was defined between them. The analysis of the animal experimental results showed correlations between tumor aggressiveness and the following factors: the normalized temperature difference, the estimated tumor state, and the temperature difference change rate. The simulated human breast cancer models analysis showed highly varying temperature differences between the simulated models. Although for each model there is a clear difference between the temperature differences of the test cases simulated, the large differences between the results might make tumor state estimation difficult. However, reviewing the gradient of the tumor temperature change as a function of tumor size showed that the ratio between the gradients of both test cases was similar for all models. Therefore, the effect of model errors and differences in the simulated tissue structure and properties and the environmental conditions between the different models, can be mitigated. This pattern may be used to estimate tumor state in in vivo experiments.

CONCLUSIONS

Continuous monitoring of tumor temperature difference produces valuable information on tumor state and aggressiveness that can be used both in the clinic and in the laboratory. Monitoring can be either performed on a single image, or continuous on multiple images, revealing changes in tumor state.

Potentialities of steady-state and transient thermography in breast tumour depth detection: A numerical study

Breast thermography still has inherent limitations that prevent it from being fully accepted as a breast screening modality in medicine. The main challenges of breast thermography are to reduce false positive results and to increase the sensitivity of a thermogram. Further, it is still difficult to obtain information about tumour parameters such as metabolic heat, tumour depth and diameter from a thermogram. However, infrared technology and image processing have advanced significantly and recent clinical studies have shown increased sensitivity of thermography in cancer diagnosis. The aim of this paper is to study numerically the possibilities of extracting information about the tumour depth from steady state thermography and transient thermography after cold stress with no need to use any specific inversion technique. Both methods are based on the numerical solution of Pennes bioheat equation for a simple three-dimensional breast model. The effectiveness of two approaches used for depth detection from steady state thermography is assessed. The effect of breast density on the steady state thermal contrast has also been studied. The use of a cold stress test and the recording of transient contrasts during rewarming were found to be potentially suitable for tumour depth detection during the rewarming process. Sensitivity to parameters such as cold stress temperature and cooling time is investigated using the numerical model and simulation results reveal two prominent depth-related characteristic times which do not strongly depend on the temperature of the cold stress or on the cooling period.

Thermal analysis of cancerous breast model

Breast cancer is one of the most common and dangerous cancers. Subsurface breast cancer lesions generate more heat and have increased blood supply when compared to healthy tissue, and this temperature rise is mirrored in the skin surface temperature. The rise in temperature on the skin surface, caused by the cancerous lesion, can be measured noninvasively using infrared thermography, which can be used as a diagnostic tool to detect the presence of a lesion. However, its diagnostic ability is limited when image interpretation relies on qualitative principles. In this study, we present a quantitative thermal analysis of breast cancer using a 3D computational model of the breast. The COMSOL FEM software was used to carry out the analysis. The effect of various parameters (tumor size, location, metabolic heat generation and blood perfusion rate) on the surface temperature distribution (which can be measured with infrared thermography) has been analyzed. Key defining features of the surface temperature profile have been identified, which can be used to estimate the size and location of the tumor based on (measured) surface temperature data. In addition, we employed a dynamic cooling process, to analyze surface temperature distributions during cooling and thermal recovery as a function of time. In this study, the effect of the cooling temperature on the enhancement of the temperature differences between normal tissue and cancerous lesions is evaluated. This study demonstrates that a quantification of temperature distributions by computational modeling, combined with thermographic imaging and dynamic cooling can be an important tool in the early detection of breast cancer.