UWB device for breast microwave imaging: phantom and clinical validations

Microwave imaging has received increasing attention in the last decades, motivated by its application in diagnostic imaging. Such effort has been encouraged by the fact that, at microwave frequencies, it is possible to distinguish between tissues with different dielectric properties. In such framework, a novel microwave device is presented here. The apparatus, consisting of two antennas operating in air, is completely safe and non-invasive since it does not emit any ionizing radiation and it can be used for breast lesion detection without requiring any breast crushing. We use Huygens Principle to provide a novel understanding into microwave imaging; specifically, the algorithm based on this principle provides images which represent homogeneity maps of the dielectric properties (dielectric constant and/or conductivity). The experimental results on phantoms having inclusions with different dielectric constants are presented here. In addition, the capability of the device to detect breast lesions has been verified through clinical examinations on 51 breasts. We introduce a metric to measure the non-homogeneous behaviour of the image, establishing a modality to detect the presence of inclusions inside phantoms and, similarly, the presence of a lesion inside a breast

Frequency Selection to Improve the Performance of Microwave Breast Cancer Detecting Support Vector Model by Using Genetic Algorithm

This paper presents an innovative paradigm for breast cancer detection by leveraging a Support Vector Machine (SVM) based model fueled with numerical data obtained from the cutting-edge MammoWave device. Operating in the microwave spectrum between 1 to 9 GHz and boasting a 5 MHz sampling rate, MammoWave emerges as a groundbreaking solution, specifically addressing the limitations posed by conventional methods, particularly for women under 50. This technological advancement opens a promising avenue for more frequent and precise breast health monitoring. To enhance the efficacy of the SVM model, our research introduces a metaheuristic-based methodology, strategically navigating the selection of frequencies crucial for breast cancer detection within the MammoWave dataset. Overcoming the challenge of judicious frequency selection, our approach employs wrapper methods in metaheuristic algorithms. These algorithms iterate through subsets of frequencies, guided by the SVM model's performance, culminating in the identification of the optimal frequency subset that significantly refines precision in breast cancer detection. Moreover, a novel cost function is proposed to strike a balanced trade-off between sensitivity and specificity, ensuring an acceptable accuracy rate. The results exhibit a noteworthy 10% increase in specificity, a milestone achievement for the MammoWave device, yielding an overall detection rate of approximately 62%. This research underscores the potential of seamlessly integrating metaheuristic algorithms into frequency selection, thereby contributing significantly to the ongoing refinement of MammoWave's capabilities in breast cancer detection

A Multicentric, Single Arm, Prospective, Stratified Clinical Investigation to Confirm MammoWave’s Ability in Breast Lesions Detection

Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, we aim to investigate through a multicentric European clinical trial (ClinicalTrials.gov Identifier NCT05300464) the effectiveness of the MammoWave microwave imaging device, which uses a Huygens-principle-based radar algorithm for image reconstruction and comprises dedicated image analysis software. A detailed clinical protocol has been prepared outlining all aspects of this study, which will involve adult females having a radiologist study output obtained using conventional exams (mammography and/or ultrasound and/or magnetic resonance imaging) within the previous month. A maximum number of 600 volunteers will be recruited at three centres in Italy and Spain, where they will be asked to sign an informed consent form prior to the MammoWave scan. Conductivity weighted microwave images, representing the homogeneity of the tissues’ dielectric properties, will be created for each breast, using a conductivity = 0.3 S/m. Subsequently, several microwave image parameters (features) will be used to quantify the images’ non-homogenous behaviour. A selection of these features is expected to allow for distinction between breasts with lesions (either benign or malignant) and those without radiological findings. For all the selected features, we will use Welch’s t-test to verify the statistical significance, using the gold standard output of the radiological study review

MammoWave Breast Imaging Device: Prospective Clinical Trial Results and AI Enhancement

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data

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

CD1a-positive infiltrating-dendritic cell density and 5-year survival from human breast cancer

© Churchill LivingstoneInfiltrating CD1a+ dendritic cells (DCs) have been associated with increased survival in a number of human cancers. This study investigated DC infiltration within breast cancers and the association with survival. Classical established prognostic factors, of tumour size, lymph node status, histological grade, lympho-vascular invasion, the KI-67 (MIB-1) fraction and the Nottingham Prognostic Index (NPI) were also compared. A total of 48 breast cancer patients were followed from the time of surgery and CD1a density analysis for 5 years or until death. Our data set validated previous studies, which show a relationship between survival and the NPI (P<0.001), tumour size (P<0.01) and lymph node status (P<0.05). Although more patients were alive at the 5-year time point in the group with higher CD1a DC density than the lower CD1a DC group, this failed to reach statistical significance at the P=0.05 level. Analysis at 10 years postsurgery is required to investigate the association further.B.J.Coventry and J. Morto