Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, 34469, Turkey.
Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, Istanbul, 34469, Turkey.
Sci Rep. 2022 Jan 10;12(1):349. 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[Formula: see text] 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[Formula: see text] 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.
乳腺癌是女性最常见的癌症类型。因此，人们正在研究用于疾病诊断和治疗的潜在新技术。一种很有前途的技术是微波应用，旨在利用恶性和正常组织之间固有的介电特性差异。从理论上讲，通过简单测量介电特性就可以对异常进行特征描述。然而，目前的测量技术容易出错，单次测量的准确性不足以高置信度地检测异常。这项工作提出了一种基于收集的大规模体内 S[Formula: see text]测量和使用圆形衍射天线的相应组织介电特性来对大鼠乳腺癌进行分类的方法。通过使用基于学习的线性分类器，可以以可重复的方式高精度地对组织进行分类。此外，还确定了最具判别力的 S[Formula: see text]测量值，令我们惊讶的是，使用判别性测量值和线性分类器，可实现 86.92%的准确率。这些发现表明，窄带微波电路可以支持天线，从而实现低成本的自动化微波诊断系统。
