Numerical sensitivity modeling for the detection of skin tumors by using tetrapolar probe

Electrical Conductivity Measurement in Human Liver Tissue: Assessment on Normal vs. Tumor Tissue and under In Vivo vs. Ex Vivo Conditions

BACKGROUND

This study evaluated electrical conductivity in human liver tissue in the 3-1000 kHz frequency range to compare normal versus tumor tissues under in vivo versus ex vivo conditions.

METHODS

Previous informed consent was obtained from twenty patients undergoing liver resection in whom liver electrical conductivity was measured during surgery and after resection.

RESULT

We found higher electrical conductivity values in tumor tissues than in normal tissue in both in vivo (0.41 ± 0.10 vs. 0.13 ± 0.06 S/m) and ex vivo (0.27 ± 0.09 vs. 0.12 ± 0.07 S/m) conditions (at 3 kHz). The electric properties also showed a promising potential for distinguishing between different tissue types including metastasis, cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC), hepatic cirrhosis, and normal liver (both in vivo and ex vivo). At 3 kHz, in vivo electrical conductivity for cholangiocarcinoma, HCC, and metastasis were 0.35, 0.42 ± 0.13, and 0.41 ± 0.08 S/m, respectively, which differed significantly from each other (p < 0.05).

CONCLUSIONS

These findings could potentially improve liver disease diagnostics through electrical conductivity measurements and treatment techniques involving electric fields. Future research should focus on expanding the sample size to refine the categorization and comparison processes across diverse human liver tissue types.

Skin layer classification by feedforward neural network in bioelectrical impedance spectroscopy

Conductivity change in skin layers has been classified by source indicator ok (k=1: Stratum corneum, k=2: Epidermis, k=3: Dermis, k=4: Fat, and k=5: Stratum corneum + Epidermis) trained from feedforward neural network (FNN) in bioelectrical impedance spectroscopy (BIS). In BIS studies, treating the skin as a bulk, limits the differentiation of conductivity changes in individual skin layers, however skin layer classification using FNN shows promise in accurately categorizing skin layers, which is essential for predicting source indicators ok and initiating skin dielectric characteristics diagnosis. The ok is trained by three main conceptual points which are (i) implementing FNN for predicting k in conductivity change, (ii) profiling four impedance inputs αξ consisting of magnitude input α|z|, phase angle input αθ, resistance input αR, and reactance input αx for filtering nonessential input, and (iii) selecting low and high frequency pair ( f r l h ) by distribution of relaxation time (DRT) for eliminating parasitic noise effect. The training data set of FNN is generated to obtain the αξ ∈ R10×17×10 by 10,200 cases by simulation under configuration and measurement parameters. The trained skin layer classification is validated through experiments with porcine skin under various sodium chloride (NaCl) solutions CNaCl = {15, 20, 25, 30, 35}[mM] in the dermis layer. FNN successfully classified conductivity change in the dermis layer from experiment with accuracy of 90.6% for the bipolar set-up at f 6 l h = 10   & 100   [ kHz] and with the same accuracy for the tetrapolar at f 8 l h = 35   & 100   [ kHz] . The measurement noise and systematic error in the experimental results are minimized by the proposed method using the feature extraction based on αξ at f r l h .

Open-Ended Coaxial Probe Technique for Dielectric Measurement of Biological Tissues: Challenges and Common Practices

Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there is a contrast in the dielectric properties of different tissue types or that the properties of particular tissues fall within a defined range. Thus, accurate knowledge of the dielectric properties of biological tissues is fundamental to EM medical technologies. Over the past decades, numerous studies were conducted to expand the dielectric repository of biological tissues. However, dielectric data is not yet available for every tissue type and at every temperature and frequency. For this reason, dielectric measurements may be performed by researchers who are not specialists in the acquisition of tissue dielectric properties. To this end, this paper reviews the tissue dielectric measurement process performed with an open-ended coaxial probe. Given the high number of factors, including equipment- and tissue-related confounders, that can increase the measurement uncertainty or introduce errors into the tissue dielectric data, this work discusses each step of the coaxial probe measurement procedure, highlighting common practices, challenges, and techniques for controlling and compensating for confounders.