An official website of the United States government
Infrared breast thermography has been associated with the early detection of breast cancer. However, findings in previous studies have been inconclusive. The upright position of subjects during imaging introduces errors in interpretation because it blocks the optical access in the inframammary fold region and alters the temperature due to contact between breast and chest wall. These errors can be avoided by imaging breasts in prone position. Although the numerical simulations provide insight into thermal characteristics of the female breast with a tumor, most simulations in the past have used cubical and hemispherical breast models. We hypothesize that a breast model with the actual breast shape will provide true thermal characteristics that are useful in tumor detection. A digital breast model in prone position is developed to generate the surface temperature profiles for breasts with tumors. The digital breast model is generated from sequential MRI images and simulations are performed using Finite Volume Method employing Pennes bioheat equation. We investigated the effect of varying the tumor metabolic activity on the surface temperature profile. We compared the surface temperature profile for various tumor metabolic activities with a case without tumor. The resulting surface temperature rise near the location of the tumor was between 0.665 and 1.023 °C, detectable using modern Infrared cameras. This is the first time that numerical simulations are conducted in a model with the actual breast shape in prone position to study the surface temperature changes induced by breast cancer.
more »
« less
Infrared imaging technology for breast cancer detection – Current status, protocols and new directions
Early and accurate detection of breast cancer is a critical part of the strategy to reduce the morbidity and mortality associated with this common disease. While current guidelines recommend mammography for screening, the sensitivity and specificity of mammograms remains less than optimal, especially for patients with dense breast tissue. Thermography has been explored in the past as an alternative to mammography. Advances in IR cameras that are used to obtain thermal images of the breast as well as computational tools used to accurately model heat transfer within the breast have significantly increased the accuracy of thermography. The current work reviews the progress that has been made in using thermal imaging to detect breast cancer over the past three decades and identifies aspects that need further refinement for it to become a reliable tool to diagnose breast cancer. Recent advances and suggestions for future work in the field including using advanced simulation methods, inverse modeling, imaging protocols, and using artificial neural networks to better predict the location of the tumor are also presented.
more »
« less
- Award ID(s):
- 1640309
- PAR ID:
- 10025814
- Date Published:
- Journal Name:
- International Journal of Heat and Mass Transfer
- Volume:
- 108
- ISSN:
- 0017-9310
- Page Range / eLocation ID:
- 2303-2320
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Breast cancer is a prevalent form of cancer among women. It is associated with increased heat generation due to higher metabolism in the tumor and increased blood vessels resulting from angiogenesis. The thermal alterations result in a change in the breast surface temperature profile. Infrared imaging is an FDA-approved adjunctive to mammography, which employs the surface temperature alterations in detecting cancer. To apply infrared imaging in clinical settings, it is necessary to develop effective techniques to model the relation between the tumor characteristics and the breast surface temperatures. The present work describes the thermal modeling of breast cancer with physics-informed neural networks. Losses are assigned to random points in the domain based on the boundary conditions and governing equations that should be satisfied. The Adam optimizer in TensorFlow minimizes the losses to find the temperature field or thermal conductivity that satisfies the boundary conditions and the bioheat equation. Backpropagation computes the derivatives in the bioheat equation. Analyses of the three patient-specific cases show that the machine-learning model accurately reproduces the thermal behavior given by ansys-fluent simulation. Also, good agreement between the model prediction and the infrared images is observed. Moreover, the neural network accurately recovers the thermal conductivity within 6.5% relative error.more » « less
-
Breast cancer is the leading cancer affecting women globally. Despite deep learning models making significant strides in diagnosing and treating this disease, ensuring fair outcomes across diverse populations presents a challenge, particularly when certain demographic groups are underrepresented in training datasets. Addressing the fairness of AI models across varied demographic backgrounds is crucial. This study analyzes demographic representation within the publicly accessible Emory Breast Imaging Dataset (EMBED), which includes de-identified mammography and clinical data. We spotlight the data disparities among racial and ethnic groups and assess the biases in mammography image classification models trained on this dataset, specifically ResNet-50 and Swin Transformer V2. Our evaluation of classification accuracies across these groups reveals significant variations in model performance, highlighting concerns regarding the fairness of AI diagnostic tools. This paper emphasizes the imperative need for fairness in AI and suggests directions for future research aimed at increasing the inclusiveness and dependability of these technologies in healthcare settings. Code is available at: https://github.com/kuanhuang0624/EMBEDFairModels.more » « less
-
Video analysis tools such as Tracker are used to study mechanical motion captured by photography. One can also imagine a similar tool for tracking thermal motion captured by thermography. Since its introduction to physics education, thermal imaging has been used to visualize phenomena that are invisible to the naked eye and teach a variety of physics concepts across different educational settings. But thermal cameras are still scarce in schools. Hence, videos recorded using thermal cameras such as those featured in “YouTube Physics” are suggested as alternatives. The downside is that students do not have interaction opportunities beyond playing those videos.more » « less
-
The mammary microbiome is a newly characterized bacterial niche that might offer biological insight into the development of breast cancer. Together with in-depth analysis of the gut microbiome in breast cancer, current evidence using next-generation sequencing and metabolic profiling suggests compositional and functional shifts in microbial consortia are associated with breast cancer. In this review, we discuss the fundamental studies that have progressed this important area of research, focusing on the roles of both the mammary tissue microbiome and the gut microbiome. From the literature, we identified the following major conclusions, (I) There are unique breast and gut microbial signatures (both compositional and functional) that are associated with breast cancer, (II) breast and gut microbiome compositional and breast functional dysbiosis represent potential early events of breast tumor development, (III) specific breast and gut microbes confer host immune responses that can combat breast tumor development and progression, and (IV) chemotherapies alter the microbiome and thus maintenance of a eubiotic microbiome may be key in breast cancer treatment. As the field expectantly advances, it is necessary for the role of the microbiome to continue to be elucidated using multi-omic approaches and translational animal models in order to improve predictive, preventive, and therapeutic strategies for breast cancer.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
