A Patient-Specific Approach for Breast Cancer Detection and Tumor Localization Using Infrared Imaging

Breast cancer (BC) is the most common cancer among women in the United States; approximately one out of every 24 women die of related causes. BC screening is a critical factor for improving patient prognosis and survival rate. Infrared (IR) thermography is an accurate, inexpensive and operator independent modality that is not affected by tissue density as it captures surface temperature variations induced by the presence of tumors. A novel patient-specific approach for IR imaging and simulation is proposed. In this work, multi-view IR images of isolated breasts are obtained in the prone position (face down), which allows access to the entire breast surface because the breasts hang freely. The challenge of accurately determining size and location of tumors within the breasts is addressed through numerical simulations of a patient-specific digital breast model. The digital breast models for individual patients are created from clinical images of the breast, such as IR imaging, digital photographs or magnetic resonance images. The numerical simulations of the digital breast model are conducted using ANSYS Fluent, where computed temperature images are generated in the same corresponding views as clinical IRI images. The computed and clinical IRI images are aligned and compared to measure their match. The determination of tumor size and location was conducted through the Levenberg-Marquardt algorithm, which iteratively minimized the mean squared error. The methodology was tested on the breasts of seven patients with biopsy-proven breast cancer with tumor diameters ranging from 8 mm to 27 mm. The method successfully predicted the equivalent tumor diameter within 2 mm and the location was predicted within 6.3 mm in all cases. The time required for the estimation is 48 minutes using a 10-core, 3.41 GHz workstation. The method presented is accurate, fast and has potential to be used as an adjunct modality to mammography in BC screening, especially for dense breasts

Breast Tumor Simulation and Parameters Estimation Using Evolutionary Algorithms

An estimation methodology is presented to determine the breast tumor parameters using the surface temperature profile that may be obtained by infrared thermography. The estimation methodology involves evolutionary algorithms using artificial neural network (ANN) and genetic algorithm (GA). The ANN is used to map the relationship of tumor parameters (depth, size, and heat generation) to the temperature profile over the idealized breast model. The relationship obtained from ANN is compared to that obtained by finite element software. Results from ANN training/testing were in good agreement with those obtained from finite element model. After ANN validation, GA is used to estimate tumor parameters by minimizing a fitness function involving comparing the temperature profiles from simulated or clinical data to those obtained by ANN. Results show that it is possible to determine the depth, diameter, and heat generation rate from the surface temperature data (with 5% random noise) with good accuracy for the 2D model. With 10% noise, the accuracy of estimation deteriorates for deep-seated tumors with low heat generation. In order to further develop this methodology for use in a clinical scenario, several aspects such as 3D breast geometry and the effects of nonuniform cooling should be considered in future investigations

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Parametric Study of Infrared Imaging Based Breast Cancer Detection Program

Breast cancer is one of the most common cancers among women and is responsible for over 41,000 lives every year in the US according to The American Cancer Society. Current screening and imaging methods such as mammography, breast magnetic resonance imaging, and breast ultrasound imaging have helped in improving survival rate when the cancer is detected at an early stage. The problems with these techniques include: low sensitivity, patient discomfort, invasiveness, and cost. Due to current advancements in infrared and computational technologies, infrared thermography has been utilized as a noninvasive adjunctive screening modality. A computerized approach using infrared imaging (IRI) has been recently developed at RIT in collaboration with Rochester General Hospital for breast cancer detection and image localization. The parameters used in this simulation have been selected based on limited information available in the literature. This study focuses on analyzing the effects of different tissue thermal parameters used in the simulation on the accuracy of prediction. Thermal conductivity and perfusion rate are systematically varied, and their effects are presented by comparing simulated images with the actual infrared images captured from a biopsy-proven breast cancer patient. The results indicate a strong influence of perfusion rate within the breast tissue surrounding the tumor on heat transfer within the breast. This study is expected to help in proper selection of thermal properties while conducting the simulations. Future directions for research are also presented

A Patient-Specific Infrared Imaging Technique for Adjunctive Breast Cancer Screening: A Clinical and Simulation - Based Approach

Breast cancer is currently the most prevalent form of cancer in women with over 266,000 new diagnoses every year. The various methods used for breast cancer screening range in accuracy and cost, however there is no easily reproducible, reliable, low-cost screening method currently available for detecting cancer in breasts, especially with dense tissue. Steady-state Infrared Imaging (IRI) is unaffected by tissue density and has the potential to detect tumors in the breast by measuring and capturing the thermal profile on the breast surface induced by increased blood perfusion and metabolic activity in a rapidly growing malignant tumor. The current work presents a better understanding of IRI as an accurate breast cancer detection modality. A detailed study utilizing IRI-MRI approach with clinical design and validation of an elaborate IRI-Mammo study are presented by considering patient population, clinical study design, image interpretation, and recommended future path. Clinical IRI images are obtained in this study and an ANSYS-based modeling process developed earlier at RIT is used to localize and detect tumor in seven patients without subjective human interpretation. Further, the unique thermal characteristics of tumors that make their signatures distinct from benign conditions are identified. This work is part of an ongoing multidisciplinary collaboration between a team of thermal engineers and numerical modelers at the Rochester Institute of Technology and a team of clinicians at the Rochester General Hospital. The following components were developed to ensure valid experimentation while considering ethical considerations: IRB documentation, patient protocols, an image acquisition system (camera setup and screening table), and the necessary tools needed for image analysis without human interpretation. IRI images in the prone position were obtained and were used in accurately detecting the presence of a cancerous tumor in seven subjects. The size and location of tumor was also confirmed within 7 mm as compared to biopsy-proven pathology information. The study indicates that the IRI-Mammo approach has potential to be a highly effective adjunctive screening tool that can improve the breast cancer detection rates especially for subjects with dense breast tissue. This method is low cost, no-touch, radiation-free and highly portable, making it an attractive candidate as a breast cancer detection modality. Further, the developed method provided insight into infrared features corresponding to other biological images, pathology reports and patient history

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

Thermal dosimetry for bladder hyperthermia treatment. An overview.

The urinary bladder is a fluid-filled organ. This makes, on the one hand, the internal surface of the bladder wall relatively easy to heat and ensures in most cases a relatively homogeneous temperature distribution; on the other hand the variable volume, organ motion, and moving fluid cause artefacts for most non-invasive thermometry methods, and require additional efforts in planning accurate thermal treatment of bladder cancer. We give an overview of the thermometry methods currently used and investigated for hyperthermia treatments of bladder cancer, and discuss their advantages and disadvantages within the context of the specific disease (muscle-invasive or non-muscle-invasive bladder cancer) and the heating technique used. The role of treatment simulation to determine the thermal dose delivered is also discussed. Generally speaking, invasive measurement methods are more accurate than non-invasive methods, but provide more limited spatial information; therefore, a combination of both is desirable, preferably supplemented by simulations. Current efforts at research and clinical centres continue to improve non-invasive thermometry methods and the reliability of treatment planning and control software. Due to the challenges in measuring temperature across the non-stationary bladder wall and surrounding tissues, more research is needed to increase our knowledge about the penetration depth and typical heating pattern of the various hyperthermia devices, in order to further improve treatments. The ability to better determine the delivered thermal dose will enable clinicians to investigate the optimal treatment parameters, and consequentially, to give better controlled, thus even more reliable and effective, thermal treatments