Empirical Assessment of Breast Lesion Detection Capability Through an Innovative Microwave Imaging Device

This paper investigates the effect of conductivity weighting on microwave images obtained through a dedicated imaging device. MammoWave is a microwave imaging device for detection of breast lesions, operating using only two azimuthally rotating antennas without the use of matching liquids. For each breast, a set of conductivity weighted images are generated through modifying our algorithm based on Huygens principle, producing intensity maps representing the homogeneity of tissues’ dielectric properties. Subsequently, we introduce several imaging parameters (i.e. features) to quantify the non-homogenous behaviour of the image. Through empirical investigation on 103 breasts, we can verify that a selection of these features could allow distinction between breasts with radiological findings (WF), i.e. with benign or malign lesions, and breasts with no radiological findings (NF). 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 0.68. Significantly, we achieve AUCs of up to 0.77 when considering dense breasts only, which tend to cause detection limitations in mammography exams

3D Huygens Principle based Microwave Imaging through MammoWave Device: Validation through Phantoms.

This work focuses on developing a 3D microwave imaging (MWI) algorithm based on the Huygens principle (HP). Specifically, a novel, fast MWI device (MammoWave) has been presented and exploited for its capabilities of extending image reconstruction from 2D to 3D. For this purpose, dedicated phantoms containing 3D structured inclusion have been prepared with mixtures having different dielectric properties. Phantom measurements have been performed at multiple planes along the z-axis by simultaneously changing the transmitter and receiver antenna height via the graphic user interface (GUI) integrated with MammoWave. We have recorded the complex S21 multi-quote data at multiple planes along the z-axis. The complex multidimensional raw data has been processed via an enhanced HP-based image algorithm for 3D image reconstruction. This paper demonstrates the successful detection and 3D visualization of the inclusion with varying dimensions at multiple planes/cross-sections along the z-axis with a dimensional error lower than 7.5%. Moreover, the paper shows successful detection and 3D visualization of the inclusion in a skull-mimicking phantom having a cylindrically shaped inclusion, with the location of the detected inclusion in agreement with the experimental setup. Additionally, the localization of a 3D structured spherical inclusion has been shown in a more complex scenario using a 3-layer cylindrically shaped phantom, along with the corresponding 3D image reconstruction and visualization

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

SKI-1 and Furin Generate Multiple RGMa Fragments that Regulate Axonal Growth

SummaryThe nervous system is enormously complex, yet the number of cues that control axonal growth is surprisingly meager. Posttranslational modifications amplify diversity, but the degree to which they are employed is unclear. Here, we show that Furin and SKI-1 combine with autocatalytic cleavage and a disulfide bridge to generate four membrane-bound and three soluble forms of the repulsive guidance molecule (RGMa). We provide in vivo evidence that these proprotein convertases are involved in axonal growth and that RGMa cleavage is essential for Neogenin-mediated outgrowth inhibition. Surprisingly, despite no sequence homology, N- and C-RGMa fragments bound the same Fibronectin-like domains in Neogenin and blocked outgrowth. This represents an example in which unrelated fragments from one molecule inhibit outgrowth through a single receptor domain. RGMa is a tethered membrane-bound molecule, and proteolytic processing amplifies RGMa diversity by creating soluble versions with long-range effects as well

Novel microwave apparatus for breast lesions detection: Preliminary clinical results

This paper presents preliminary results of an innovative microwave imaging apparatus for breast lesions detection. Specially, a Huygens Principle based method is employed to process the microwave signals and to build the respective microwave images. The apparatus has been first tested on phantoms. Next, its performance has been verified through clinical examinations on 22 healthy breasts and on 29 breast having lesions, using as gold standard the output of the radiologist study review obtained using conventional techniques. Specifically, we introduce a metric, which is the ratio between maximum and average of the image intensity (MAX/AVG). We found that MAX/AVG of microwave images can be used for classifying breasts containing lesions. In addition, using MAX/AVG as classification parameter, receiver operating characteristic curves have been empirically determined. Furthermore, for one randomly selected breast having lesion, we have demonstrated that the localisation of the inclusion acquired through microwave imaging is compatible with mammography images

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

Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy

Dynamics of disordered systems

Disordered systems are ubiquitous in nature and their study is complicated and often leads to controversial results. In any case, the important role of such systems in science and technological applications should not be ignored. The characteristic properties of such systems seem to be driven by a fundamental feature, the degrees of freedom. Although many problems still remain matter of debate, the challenge posed in recent decades in the understanding of the impact of disorder in the physical behavior of materials is of considerable scientic interest. An exact description of a disordered phase is not possible since it is a many-body problem hard to model. However, for some materials it is possible, upon cooling, to preserve the disordered liquid-like structure having a state of high regularity. Therefore, for the so-called glass formers, it is possible to freeze some degrees of freedom obtaining a glass that presents the irregularity of a liquid with the high viscosity of a solid below the melting temperature Tm. The aim of the present PhD thesis is to present our understanding of disorder in related experimental approaches using three different pure compounds: two plastic crystals (1-Chloroadamantane and Freon113) and a liquid (Glycerol). To understand the behavior of these kinds of materials neutron scattering and dielectric spectroscopy have been used. These two powerful techniques allow us to investigate the dynamics of disordered phases on a picosecond time scale. Furthermore, given the complexity of these disordered phases, data analysis and model selection have been performed with a Bayesian approach that provides a solid statistical ground bases on probability distribution functions. Such methods have been applied to study of the above mentioned compounds dynamics in order to give an explanation of some open questions: the microscopic origin of the plastic-plastic transition in 1-chloroadamantane (C10H15Cl), the high fragility and the correlation between kinetic and thermodynamic fragility in freon113 (Cl2FC-CClF2) and the dynamics, accompanied by a robust model selection, of one of the most studied glass former compound, glycerol (C3H8O3). In addition, a brief overview of the theoretical background for neutron scattering and dielectric spectroscopy, as well as a description of the experimental setup and the consequent data treatment and analysis, are given to deliver a comprehensive and consistent view of the topic under consideration. The results, presented in this work of thesis, represent a small step in a deeper understanding of disordered phases dynamics, giving a base for further investigations.Los sistemas desordenados son ubicuos en la naturaleza y su estudio es complicado y con frecuencia conduce a resultados controvertidos. En cualquier caso, el papel importante de este tipo de sistemas en aplicaciones científicas y tecnológicas no debe ser ignorada. Las propiedades características de tales sistemas parecen estar impulsadas por una característica fundamental, los grados de libertad. Aunque muchos problemas siguen siendo materia de debate, el desafío planteado en las últimas décadas en el entendimiento del impacto del desorden en el comportamiento físico de los materiales es de considerable interés científico. Una descripción exacta de una fase desordenada no es posible, ya que es un problema de muchos cuerpos difícil de modelar. Sin embargo, para algunos materiales, es posible, tras el enfriamiento, conservar la estructura desordenada del líquido con un estado de alta regularidad. Por lo tanto, para los denominados glass-formers, es posible congelar algunos grados de libertad obteniendo un vidrio que presenta la irregularidad de un líquido con la alta viscosidad de un sólido por debajo de la temperatura de fusión Tm. El objetivo de la presente tesis doctoral es presentar nuestra comprensión del desorden en los enfoques experimentales relacionados utilizando tres diferentes compuestos puros: dos cristales de plástico (1-Chloroadamantane y Freon113) y un líquido (Glycerol). Para entender el comportamiento de este tipo de materiales se han utilizado scattering de neutrones y espectroscopía dieléctrica. Estas dos técnicas nos permiten investigar la dinámica de las fases desordenadas en una escala de tiempo de picosegundos. Por otra parte, dada la complejidad de estas fases desordenadas, análisis de datos y la selección del modelo se han realizado con un enfoque bayesiano que proporciona una sólida base estadística basada sobre las funciones de distribución de probabilidad. Tales métodos se han aplicado al estudio de la dinámica de los compuestos antes mencionados con el fin de dar una explicación de algunas preguntas abiertas: el origen microscópico de la transición plástico-plástico en 1-chloroadamantane (C10H15Cl), la alta fragilidad y la correlación entre la fragilidad cinética y termodinámica en freon113 (Cl2FC-CClF2) y la dinámica, acompañada por una robusta selección de modelo, de uno de los compuestos más estudiados, glycerol (C3H8O3). Los resultados, presentados en este trabajo de tesis, representan un pequeño paso para una comprensión más profunda de la dinámica de las fases desordenadas, dando una base para futuras investigaciones

MammoWave Breast Imaging Device: Path to Clinical Validation, Results and Implications in Future Population-based Breast Screening Programs

Microwave imaging for breast cancer detection has attracted growing global attention with a small number of prototypes advancing to the clinical trial stage. This investigation aims to provide an overview of MammoWave, a novel microwave-based imaging system for breast lesion detection and to assess its introduction into the clinical routine and its potential role in future breast screening programs. As a key focus of this work, we will describe in detail the various aspects of the clinical protocol procedure that has enabled us to perform a successful clinical trial. Obtained preliminary results indicate the ability of our device to distinguish breasts with no radiological finding and those with radiological findings, with a sensitivity of 89.6%