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High-Frequency (30 MHz–6 GHz) Breast Tissue Characterization Stabilized by Suction Force for Intraoperative Tumor Margin Assessment

A gigahertz (GHz) range antenna formed by a coaxial probe has been applied for sensing cancerous breast lesions in the scanning platform with the assistance of a suction tube. The sensor structure was a planar central layer and a metallic sheath of size of 3 cm(2) connected to a network analyzer (ke...

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Detalles Bibliográficos
Autores principales: Mokhtari Dowlatabad, Hadi, Mamdouh, Amir, Yousefpour, Narges, Mahdavi, Reihane, Zandi, Ashkan, Hoseinpour, Parisa, Moosavi-Kiasari, Seyed Mohammad Sadegh, Abbasvandi, Fereshte, Kordehlachin, Yasin, Parniani, Mohammad, Mohammadpour-Aghdam, Karim, Faranoush, Pooya, Foroughi-Gilvaee, Mohammad Reza, Abdolahad, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857665/
https://www.ncbi.nlm.nih.gov/pubmed/36672989
http://dx.doi.org/10.3390/diagnostics13020179
Descripción
Sumario:A gigahertz (GHz) range antenna formed by a coaxial probe has been applied for sensing cancerous breast lesions in the scanning platform with the assistance of a suction tube. The sensor structure was a planar central layer and a metallic sheath of size of 3 cm(2) connected to a network analyzer (keySight FieldFox N9918A) with operational bandwidth up to 26.5 GHz. Cancer tumor cells have significantly higher water content (as a dipolar molecule) than normal breast cells, changing their polarization responses and dielectric losses to incoming GHz-based stimulation. Principal component analysis named S(11), related to the dispersion ratio of the input signal, is used as a parameter to identify malignant tumor cells in a mouse model (in vivo) and tumor specimens of breast cancer patients (in vitro) (both central and marginal parts). The results showed that S(11) values in the frequency range from 5 to 6 GHz were significantly higher in cancer-involved breast lesions. Histopathological analysis was the gold standard for achieving the S(11) calibration to distinguish normal from cancerous lesions. Our calibration on tumor specimens presented 82% positive predictive value (PPV), 100% negative predictive value (NPV), and 86% accuracy. Our goal is to apply this system as an in vivo non-invasive tumor margin scanner after further investigations in the future.