Parameter estimation of breast tumour using dynamic neural network from thermal pattern

Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation

Effective treatment of breast cancer relies heavily on early detection. Routine annual mammography is a widely accepted screening technique that has resulted in significantly improving the survival rate. However, it suffers from low sensitivity resulting in high false positives from screening. To overcome this problem, adjunctive technologies such as ultrasound are employed on about 10% of women recalled for additional screening following mammography. These adjunctive techniques still result in a significant number of women, about 1.6%, who undergo biopsy while only 0.4% of women screened have cancers. The main reason for missing cancers during mammography screening arises from the masking effect of dense breast tissue. The presence of a tumor results in the alteration of temperature field in the breast, which is not influenced by the tissue density. In the present paper, the IRI-Numerical Engine is presented as an adjunct for detecting cancer from the surface temperature data. It uses a computerized inverse heat transfer approach based on Pennes's bioheat transfer equations. Validation of this enhanced algorithm is conducted on twenty-three biopsy-proven breast cancer patients after obtaining informed consent under IRB protocol. The algorithm correctly predicted the size and location of cancerous tumors in twenty-four breasts, while twenty-two contralateral breasts were also correctly predicted to have no cancer (one woman had bilateral breast cancer). The tumors are seen as highly perfused and metabolically active heat sources that alter the surface temperatures that are used in heat transfer modeling. Furthermore, the results from this study with twenty-four biopsy-proven cancer cases indicate that the detection of breast cancer is not affected by breast density. This study indicates the potential of the IRI-Numerical Engine as an effective adjunct to mammography. A large scale clinical study in a statistically significant sample size is needed before integrating this approach in the current protocol.

Novel design of weighted differential evolution for parameter estimation of Hammerstein-Wiener systems

INTRODUCTION

Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability.

OBJECTIVES

The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters.

METHODS

The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials.

RESULTS

Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme.

CONCLUSION

Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios.

Computational Simulation of Breast Tissue with Lesion Characterized by a Thermal Gradient Oriented to Anomalies Smaller than 1 cm of Diameter

In this work, the computational simulation of thermal gradients related to internal lesions according to the phenomenon of pathological angiogenesis is proposed, this is based on the finite element method, and using a three¬dimensional geometric model adjusted to suit the real female anatomy. The simulation of the thermal distribution was based on the bioheating equation; it was carried out using the COMSOL Multiphysics® software. As a result, the simulation of both internal and superficial thermal distributions associated to lesions smaller than 1 cm and located inside the simulated breast tissue were obtained. An increase in temperature on the surface of the breast of 0.1 ° C was observed for a lesion of 5 mm in diameter and 15 mm in deep. A qualitative validation of the model was carried out by contrasting the simulation of anomalies of 10 mm in diameter at different depths (10, 15 and 20 mm) proposed in the literature, with the simulation of the model proposed here, obtaining the same behavior for the three cases.Clinical Relevance- The 3D computational tool adjusted to suit the anatomy of the real female breast allows obtaining the temperature distribution inside and on the surface of the tissue in healthy cases and with abnormalities associated with temperature elevations. It is an important characteristic of the model when the behavior of the parameters inside the tissue needs to be analyzed.

A Computational Method for the Estimation of the Geometrical and Thermophysical Properties of Tumor Using Contact Thermometry

Contact thermometry is the measurement of surface temperature using sensors in contact with the medium. These surface temperatures can be potential indicators of any abnormality possibly a tumor. This research work aims to present a computation method that makes use of contact thermometry to estimate the geometric center, size, and thermophysical properties of breast tumor. Wearable thermal sensors captured real-time surface temperature readings from discrete point locations. The continuous heat distribution over the domain was formulated using forward heat transfer analysis. The optimization method estimated tumor parameters of the breast, and a three-dimensional thermal model was developed from the estimated parameters. Laboratory experiments on breast phantoms were done to validate the estimation method. Furthermore, real-time temperature readings of human subjects were recorded, and the estimated location and size were then compared with the mammogram results. It was found that the estimated two-dimensional geometric center and the size in diameter of the tumor closely match with the mammogram results. Further, the thermophysical properties estimated using the proposed method had a higher order in subjects having a tumor making it a tool for breast cancer screening.

Breast tumor parameter estimation and interactive 3D thermal tomography using discrete thermal sensor data

This work uses a simple low-cost wearable device embedded with discrete thermal sensors to map the breast skin surface temperature. A methodology has been developed to estimate diameter, blood perfusion, metabolic heat generation and location in X, Y, Z coordinate of tumor from this discrete set of data. An interactive 3D thermal tomography was developed which provides a detailed 3D thermal view of the breast anatomy. Using this system, the user can interactively rotate and slice the 3D thermal image of the breast for a detailed study of the tumor. Finite element method (FEM) and an evolution-based inverse method were used for the parameter estimation. The method was first validated using phantom experiments and the results obtained were within an error of 10% (0.005 W cm^-3) for heat generation and 15% (0.3 cm) for heater location. Further validation was carried out through clinical trials on 60 human subjects. Estimated blood perfusion rate and metabolic heat generation rate exhibit distinguishable difference between cancerous and non-cancerous breast. Estimated diameter and location of tumor in cancerous breast shows good agreement with the actual clinical reports. We have obtained a sensitivity of 82.78% and specificity of 87.09%. Proposed breast tumor parameter estimation methodology with interactive 3D thermal tomography is a good screening tool for breast cancer detection and also useful for clinicians to find out location including depth.

A decision support scheme for beta thalassemia and HbE carrier screening

The most effective way to combat β-thalassemias is to prevent the birth of children with thalassemia major. Therefore, a cost-effective screening method is essential to identify β-thalassemia traits (BTT) and differentiate normal individuals from carriers. We considered five hematological parameters to formulate two separate scoring mechanisms, one for BTT detection, and another for joint determination of hemoglobin E (HbE) trait and BTT by employing decision trees, Naïve Bayes classifier, and Artificial neural network frameworks on data collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, India. We validated both the scores on two different data sets and found 100% sensitivity of both the scores with their respective threshold values. The results revealed the specificity of the screening scores to be 79.25% and 91.74% for BTT and 58.62% and 78.03% for the joint score of HbE and BTT, respectively. A lower Youden's index was measured for the two scores compared to some existing indices. Therefore, the proposed scores can obviate a large portion of the population from expensive high-performance liquid chromatography (HPLC) analysis during the screening of BTT, and joint determination of BTT and HbE, respectively, thereby saving significant resources and cost currently being utilized for screening purpose.

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.

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.