Microwave Imaging to Improve Breast Cancer Diagnosis

Breast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging. Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas. The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection

Feasibility of the Detection of Breast Cancer Using Ultra-Wide Band (UWB) Technology in Comparison with Other Screening Techniques

Breast cancer is considered a leading cause of deaths among women. Researches state that women around the world still face this problem, and because of its unawareness, it is many times left unattended in the budding stages. If correctly screened and detected early, then with proper treatment, this could stop the metastasis and reduce the pains and difficulties of the later stages. Screening methods such as x-ray-based mammography, ultrasound, PET scan, and magnetic resonance imaging (MRI) clinically exist for breast tumor investigation. It is very important that screening procedures should have high specificity and sensitivity for the detection of tumors. Additionally, these methods also have to placate concerns such as ease of the patient during imaging, high-resolution images for added precise elucidation, cost effectiveness, and the capacity to detect the malignant-leading tumors in the early stage. Existing imaging techniques do not meet all of these conditions concurrently. In this scenario, ultra-wide band (UWB) technology has come into play the role of a useful alternative for screening and detection of breast tumors. This chapter discusses firstly probabilistic qualitative metrics which are used in measuring the quality of testing procedures, and then later UWB testing methods are discussed in brief

Experimental Evaluation of an Axillary Microwave Imaging System to Aid Breast Cancer Staging

The number of metastasised Axillary Lymph Nodes (ALNs) is a key indicator for breast cancer staging. Its correct assessment affects subsequent therapeutic decisions. Common ALN screening modalities lack high enough sensitivity and specificity. Level I ALNs produce detectable backscattering of microwaves, opening the way for Microwave Imaging (MWI) as a complementary screening modality. Radar-based MWI is a low-cost, noninvasive technique, widely studied for breast cancer and brain stroke detection. However, new specific challenges arise for ALN detection, which deter a simple extension of existing MWI methods. The geometry of the axillary region is more complex, limiting the antenna travel range required for maximum resolution. Additionally, unlike breast MWI setups, it is impractical to use liquid immersion to enhance energy coupling to the body; therefore, higher skin reflection masks ALNs response. We present a complete study that proposes dedicated imaging algorithms to detect ALNs dealing with the above constraints, and evaluate their effectiveness experimentally. We describe the developed setup based on a 3D-printed anthropomorphic phantom, and the antenna-positioning configuration. To the authors’ knowledge, this is the first ALN-MWI study involving a fully functional anatomically compliant setup. A Vivaldi antenna, operating in a monostatic radar mode at 2-5 GHz, scans the axillary region. Pre-clinical assessment in different representative scenarios shows Signal-to-ClutterRatio higher than 2.8 dB and Location Error lower than 15mm, which is smaller than considered ALN dimensions. Our study shows promising level I ALN detection results despite the new challenges, confirming MWI potential to aid breast cancer staging.info:eu-repo/semantics/publishedVersio

Reconstruction of Microwave Imaging using Machine Learning

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasBreast cancer is the most diagnosed cancer in women. The gold standard technique for mass screening is X-ray mammography, which requires the use of ionising radiation. Mammography has a high false positive rate for women under 50, since the technique is highly sensitive to breast density. Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Ultrasound Imaging (US) have been suggested as complementary imaging tools to lessen the false positive results; however present some disadvantages. The potential of using microwave signals for breast cancer detection and monitoring has been studied for over 20 years. Microwave Breast Imaging (MBI) is a low-cost, non-invasive and non-ionising technique. The reflected microwave signals are transformed into an image via beamforming algorithms. These images have limited resolution, which may result in a considerable high rate of false positives and false negatives. In this dissertation, a complementary method of image reconstruction using Machine Learning (ML) models to predict the healthy or tumorous nature of breast is proposed. To study the potential of the proposed method, microwave signals were collected with a monostatic radar-based microwave system. The signal was acquired from three breast phantoms: one mimicking a homogeneous breast and two mimicking heterogeneous breasts. The phantoms had a cavity to introduce a plug, which included types of tumour models in terms of malignancies. From the signals, portions with and without tumour signature were extracted to train classification models. The most robust models were used to reconstruct a binary image of the breast with values of “hit” for tumorous focal points, and values of “miss” for healthy focal points. Eventually, the reconstructed images resulting from the proposed method were compared with the images obtained using the traditional beamforming method, DAS. Overall, the results obtained with the method ML-based were satisfactory, since for most phantoms the regions classified as tumour, indeed corresponded to the real position of the tumour

Evaluating a breast tumor monitoring vest with flexible UWB antennas and realistic phantoms:a proof-of-concept study

Abstract. The introduction provides an overview of the global significance of breast cancer as a health concern and the limitations of existing breast cancer screening methods. It introduces the concept of microwave-based breast cancer monitoring and highlights the promising findings from a previous research paper. The objective of the master thesis is presented, which is to develop and evaluate a self-monitoring vest equipped with UWB antennas and channel analysis to overcome the limitations of current screening methods and enable regular breast cancer monitoring from home. The "Background and Literature Review," provides a comprehensive overview of the relevant topics related to microwave techniques for breast cancer detection. It starts by discussing the anatomy of the female breast, highlighting the importance of understanding its structure for effective tumor detection. The section then delves into the microwave properties of the human breast, elucidating the interactions between microwaves and breast tissue. The basic principle of microwave channel analysis is explained, emphasizing its significance in detecting breast tumors. Furthermore, the advantages of microwave-based tumor detection methods are explored, showcasing their potential for improved breast cancer screening. Various microwave techniques used in breast cancer detection, including microwave tomography and radar-based UWB microwave imaging, are discussed, along with different self-monitoring vests integrated with UWB antennas. This section serves as a foundation for the subsequent chapters of the thesis, providing a comprehensive background and literature review to support the research and development of the practical self-monitoring vest for early detection of small-sized breast tumors. The "Preparation of Tissue Phantoms" section in the master’s thesis explores the comprehensive methodology for creating tissue phantoms that replicate the dielectric properties of various human tissues. While the section primarily focuses on fat tissue, it also acknowledges the existence of other phantom types. The outlined approach involves careful ingredient selection, formulation development, fabrication techniques, and stability evaluation for the creation of skin, muscle, fat, tumor, and gland tissue phantoms. By following these procedures, researchers can successfully produce tissue phantoms that closely mimic the properties of real human tissues. These phantoms serve as essential tools for investigating microwave-based applications in medical diagnostics and provide a reliable and versatile platform for further research in the field. The third section discusses the assembly of heterogeneous breast phantoms used for evaluating the performance of the tumor detection vest. The phantoms consisted of outer and inner molds, with the outer molds resembling the shape of a prone human breast. Two breast density types, representing very dense and less dense breasts, were used. For the dense breast phantoms, liquid fat material was solidified in the outer molds, and a glandular liquid was poured into the inner mold, with tumors inserted and covered with additional glandular liquid. For the less dense breast phantoms, fat liquid was solidified in the outer molds, and cylindrical glandular molds were inserted. A skin layer and muscle layer were added to complete the assembly, accurately simulating the composition and structure of a breast. This realistic breast phantom assembly allowed for accurate measurements and evaluation of the vest’s performance under different breast density conditions, contributing to breast imaging research and development. The "Monitoring Vest" section discusses the antennas used in the tumor detection vest and the design of two different vest versions. Antenna1 is a UWB monopole antenna with a flexible laminate substrate, while Antenna2 is a textile-based version of Antenna1. Antenna3 has a Kapton-based substrate and larger dimensions. The combination of these antennas ensures accurate tumor detection in various breast conditions. The section also highlights the measurement and comparison of the S11 parameter for the PCB antenna in free space and when placed on the skin, emphasizing the impact of the skin on antenna performance. The section concludes by describing the design of the vests, including the arrangement of pockets and the use of RF cables for connection. The careful design and implementation of the vests and antenna placement ensure accurate measurements and reliable performance evaluation. The results section of the study shows that the presence of tumors in breast tissue leads to a noticeable decrease in channel attenuation. The higher dielectric properties of tumors cause additional reflections and diffraction, affecting signal propagation within the breast. These changes in channel characteristics are influenced by factors such as tumor size, breast density, and antenna configuration. The study demonstrates the detectability of tumors and provides valuable insights for developing effective tumor detection systems in different breast tissue scenarios. In this master thesis, a prototype of a breast tumor monitoring vest utilizing UWB flexible antennas was developed and evaluated. The research demonstrated the effectiveness of the vest in detecting breast tumors, even as small as 1cm, by leveraging the distinct characteristics of radio channels among multiple on-body antennas embedded in the vest. Higher frequencies in the 7–8 GHz range showed improved resolution and contrast in relative permittivity, enhancing the accuracy of tumor detection. The development of tissue phantoms played a crucial role, enabling reliable experiments to mimic human tissues. Integration of advanced AI algorithms and 6G technology holds promise for enhancing diagnostic capabilities and revolutionizing healthcare. Overall, the breast tumor monitoring vest shows potential for widespread implementation in breast health checks, home monitoring, and wireless healthcare systems

Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images' parameters.

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues' dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images' parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields