Spurious contributions from electrode polarization (EP) are a major nuisance in dielectric measurements of biological tissues and hamper accurate determination of tissue properties in the audio/radiofrequencies. Various electrode geometries and/or treatments have been employed traditionally to reduce EP contributions, although none succeeded to completely remove EP from measurements on tissues for all practical frequency ranges. A method of correction for contributions of EP to the dielectric properties of tissues is proposed. The method is based on modeling the electrode impedance with suitable functions and on the observation that certain parameters are only dependent on electrodes properties and can thus be determined separately. The method is tested on various samples with known properties, and its usefulness is demonstrated with samples of normal and cancerous human female breast tissue. It is observed that the dielectric properties of the tissues over the frequency range 40 Hz-100 MHz are significantly different among different types of breast tissue. This observation is used further to demonstrate that, by scanning the tip of the measuring dielectric probe (with modest spatial resolution) across a sample of excised breast tissue, significant variations in the electrical properties are detected at a position where a tumor is located. This study shows that dielectric spectroscopy has the potential to offer a viable alternative to the current methods for detection of breast cancer in vivo.

The results show that the Cole frequency alone is a viable classifier for malignant breast anomalies. Results of the current work are consistent with recent bioimpedance measurements on single cell and cell suspension breast cell lines.

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Dermoscopy and patch testing in patients with lichen planus pigmentosus on face: a cross-sectional observational study in fifty Indian patients.

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