Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Biomedical Engineering Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Technol Health Care. 2022;30(3):633-645. doi: 10.3233/THC-213096.
The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε´) and conductivity (σ).
To examine if the dielectric behavior of normal and malignant tissues at low frequencies (α dispersion) will lead to the threshold (separating) line between them and find the threshold values of capacitance and resistance. These data are used as input for deep learning neural networks, and the outcomes are normal or malignant.
ε´ and σ in the range of 50 Hz to 100 KHz for 15 human malignant tissues and their corresponding normal ones have been measured. The separating line equation between the two classes is found by mathematical calculations and verified via support vector machine (SVM). Normal range and the threshold value of both normal capacitance and resistance are calculated.
Deep learning analysis has an accuracy of 91.7%, 85.7% sensitivity, and 100% specificity for instant and automatic prediction of the type of breast tissue, either normal or malignant.
These data can be used in both cancer diagnosis and prognosis follow-up.
早期发现人类乳腺癌代表着极大的生存机会。恶性组织的含水量和电解质浓度较高，而脂肪含量较低。这些癌症的生化特征使恶性组织具有较高的介电常数（ε´）和电导率（σ）。
检查正常和恶性组织在低频（α 弥散）下的介电行为是否会导致它们之间的阈值（分离）线，并找到电容和电阻的阈值。这些数据可作为深度学习神经网络的输入，其结果为正常或恶性。
测量了 15 个人类恶性组织及其相应正常组织在 50 Hz 至 100 kHz 范围内的 ε´和 σ。通过数学计算找到两类之间的分离线方程，并通过支持向量机（SVM）进行验证。计算了正常范围和正常电容及电阻的阈值。
深度学习分析对即时和自动预测乳腺组织的类型（正常或恶性）的准确性为 91.7%，灵敏度为 85.7%，特异性为 100%。
这些数据可用于癌症诊断和预后随访。
