Classification of rat mammary carcinoma with large scale in vivo microwave measurements

• Published in: Scientific reports Vol. 12; no. 1; pp. 349 - 11
• Main Authors: Onemli, Emre, Joof, Sulayman, Aydinalp, Cemanur, Pastacı Özsobacı, Nural, Ateş Alkan, Fatma, Kepil, Nuray, Rekik, Islem, Akduman, Ibrahim, Yilmaz, Tuba
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 639/166/985; 639/166/987; 9,10-Dimethyl-1,2-benzanthracene; Animals; Breast cancer; Cancer; Carcinoma; Carcinoma - chemically induced; Carcinoma - classification; Carcinoma - diagnosis; Carcinoma - pathology; Dielectric properties; Electric Conductivity; Electrical properties; Electrodiagnosis; Female; Humanities and Social Sciences; Machine Learning; Mammary gland; Mammary Neoplasms, Experimental - chemically induced; Mammary Neoplasms, Experimental - classification; Mammary Neoplasms, Experimental - diagnosis; Mammary Neoplasms, Experimental - pathology; Measurement techniques; Microwaves; multidisciplinary; Predictive Value of Tests; Rats; Rats, Sprague-Dawley; Reproducibility of Results; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-03884-7

Mammary carcinoma, breast cancer, is the most commonly diagnosed cancer type among women. Therefore, potential new technologies for the diagnosis and treatment of the disease are being investigated. One promising technique is microwave applications designed to exploit the inherent dielectric property discrepancy between the malignant and normal tissues. In theory, the anomalies can be characterized by simply measuring the dielectric properties. However, the current measurement technique is error-prone and a single measurement is not accurate enough to detect anomalies with high confidence. This work proposes to classify the rat mammary carcinoma, based on collected large-scale in vivo S 11 measurements and corresponding tissue dielectric properties with a circular diffraction antenna. The tissues were classified with high accuracy in a reproducible way by leveraging a learning-based linear classifier. Moreover, the most discriminative S 11 measurement was identified, and to our surprise, using the discriminative measurement along with a linear classifier an 86.92% accuracy was achieved. These findings suggest that a narrow band microwave circuitry can support the antenna enabling a low-cost automated microwave diagnostic system.