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.

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1–9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist’s conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated.

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 behavior 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.