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.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Adult
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Aged
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Algorithms
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Area Under Curve
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Breast / diagnostic imaging*
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Breast Neoplasms / diagnosis
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Female
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Humans
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Mammography / instrumentation
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Mammography / methods*
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Microwave Imaging
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Middle Aged
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ROC Curve
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Sensitivity and Specificity
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Young Adult
Grants and funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 830265. This project leading to this application has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 793449. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 872752. Lorenzo Sani, Alessandro Vispa, Daniel Alvarez Sánchez-Bayuela, Stefano Caschera, Martina Paoli, Alessandra Bigotti, Mario Badia, Michele Scorsipa and Giovanni Raspa are employed by UBT - Umbria Bioengineering Technologies. The funder provided support in the form of salaries for such authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.