Quick, Single-Frequency Dielectric Characterization of Blood Samples of Pediatric Cancer Patients by a Cylindrical Capacitor: Pilot Study

Measurement and image-based estimation of dielectric properties of biological tissues —past, present, and future—

The dielectric properties of biological tissues are fundamental pararmeters that are essential for electromagnetic modeling of the human body. The primary database of dielectric properties compiled in 1996 on the basis of dielectric measurements at frequencies from 10 Hz to 20 GHz has attracted considerable attention in the research field of human protection from non-ionizing radiation. This review summarizes findings on the dielectric properties of biological tissues at frequencies up to 1 THz since the database was developed. Although the 1996 database covered general (normal) tissues, this review also covers malignant tissues that are of interest in the research field of medical applications. An intercomparison of dielectric properties based on reported data is presented for several tissue types. Dielectric properties derived from image-based estimation techniques developed as a result of recent advances in dielectric measurement are also included. Finally, research essential for future advances in human body modeling is discussed.

Development and evaluation of cancer differentiation analysis technology: a novel biophysics-based cancer screening method

BACKGROUND

Routine health checkup is an essential strategy for monitoring population health and maintaining healthy workforces. However, there was a lack of cancer screening tests among routine health checkups due to high costs and unreliable methods.

METHODS

We conducted a two-stage study to evaluate the value of a blood test, Cancer Differentiation Analysis (CDA^TM), which is developed to differentiate the blood samples of healthy individuals from those of cancer patients through measuring and analyzing multiple biophysical properties.

RESULTS

The first stage of a cross-sectional study included 75,942 healthy individuals in routine health checkup, and the second stage of a prospective population-based cohort included 1,957 healthy community members. Forty-eight and ten cancer cases were identified among cross-sectional study and prospective population-based cohort, respectively. Using a pre-determined cutoff, we found that the CDA™ test could differentiate blood samples between healthy and cancer individuals with >93% specificity and >55% sensitivity in both studies.

CONCLUSIONS

With high specificity and moderate sensitivity of CDA™ test, our study indicates that we can analyze biophysical properties in the blood to rapidly and reliably screen healthy individuals from cancer patients in a health checkup setting where most individuals are healthy or with average risk of cancer.

Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer

BACKGROUND

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).

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

These data can be used in both cancer diagnosis and prognosis follow-up.