Literature DB >> 33848318

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

Lorenzo Sani1, Alessandro Vispa1, Riccardo Loretoni2, Michele Duranti3, Navid Ghavami1, Daniel Alvarez Sánchez-Bayuela1, Stefano Caschera1, Martina Paoli1, Alessandra Bigotti1, Mario Badia1, Michele Scorsipa1, Giovanni Raspa1, Mohammad Ghavami4, Gianluigi Tiberi1,4.   

Abstract

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.

Entities:  

Year:  2021        PMID: 33848318     DOI: 10.1371/journal.pone.0250005

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  3 in total

1.  An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing.

Authors:  Tyson Reimer; Stephen Pistorius
Journal:  Sensors (Basel)       Date:  2021-12-07       Impact factor: 3.576

2.  Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network.

Authors:  Maitreyee Dey; Soumya Prakash Rana; Riccardo Loretoni; Michele Duranti; Lorenzo Sani; Alessandro Vispa; Giovanni Raspa; Mohammad Ghavami; Sandra Dudley; Gianluigi Tiberi
Journal:  PLoS One       Date:  2022-07-21       Impact factor: 3.752

3.  Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection.

Authors:  Mio Adachi; Tsuyoshi Nakagawa; Tomoyuki Fujioka; Mio Mori; Kazunori Kubota; Goshi Oda; Takamaro Kikkawa
Journal:  Diagnostics (Basel)       Date:  2021-12-23
  3 in total

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