Cervical Cancer Prognosis and Diagnosis Using Electrical Impedance Spectroscopy.

Electrical impedance spectroscopy (EIS) has been used as an adjunct to colposcopy for cervical cancer diagnosis for many years, Currently, the template match method is employed for EIS measurements analysis, where the measured EIS spectra are compared with the templates generated from three-dimensional finite element (FE) models of cancerous and non-cancerous cervical tissue, and the matches between the measured EIS spectra and the templates are then used to derive a score that indicates the association strength of the measured EIS to the High-Grade Cervical Intraepithelial Neoplasia (HG CIN). These FE models can be viewed as the computational versions of the associated physical tissue models. In this paper, the problem is revisited with an objective to develop a new method for EIS data analysis that might reveal the relationship between the change in the tissue structure due to disease and the change in the measured spectrum. This could provide us with important information to understand the histopathological mechanism that underpins the EIS-based HG CIN diagnostic decision making and the prognostic value of EIS for cervical cancer diagnosis. A further objective is to develop an alternative EIS data processing method for HG CIN detection that does not rely on physical models of tissues so as to facilitate extending the EIS technique to new medical diagnostic applications where the template spectra are not available. An EIS data-driven method was developed in this paper to achieve the above objectives, where the EIS data analysis for cervical cancer diagnosis and prognosis were formulated as the classification problems and a Cole model-based spectrum curve fitting approach was proposed to extract features from EIS readings for classification. Machine learning techniques were then used to build classification models with the selected features for cervical cancer diagnosis and evaluation of the prognostic value of the measured EIS. The interpretable classification models were developed with real EIS data sets, which enable us to associate the changes in the observed EIS and the risk of being HG CIN or developing HG CIN with the changes in tissue structure due to disease. The developed classification models were used for HG CIN detection and evaluation of the prognostic value of EIS and the results demonstrated the effectiveness of the developed method. The method developed is of long-term benefit for EIS-based cervical cancer diagnosis and, in conjunction with standard colposcopy, there is the potential for the developed method to provide a more effective and efficient patient management strategy for clinic practice.

Electrical Impedance Spectroscopy to Aid Parathyroid Identification and Preservation in Central Compartment Neck Surgery: A Proof of Concept in a Rabbit Model.

BACKGROUND: Accurate identification of parathyroid glands during thyroid surgery is crucial to avoid postthyroidectomy hypocalcemia. Electrical impedance spectroscopy has the potential to differentiate between tissues of different morphology. The aim of this study was to determine the electrical impedance patterns of the thyroid, parathyroid, and other soft tissue structures in the rabbit neck. METHODS: The central compartments were exposed in 9 freshly culled New Zealand White rabbits. In situ and ex vivo electrical impedance was measured from thyroid lobes, external parathyroid glands, adipose tissue, and strap muscle using the APX100 device. Specimens of all identified glands were sent for histopathology examination. RESULTS: Histology confirmed correct identification of all excised thyroid and parathyroid glands. The impedance was higher for thyroid tissue at lower frequencies and for parathyroid tissue at higher frequencies. Ex vivo electrical impedance spectra were significantly higher compared with the in situ spectra across all frequencies for thyroid and parathyroid tissues (P < .001). The ratio of low to high frequency in situ impedance of thyroid, parathyroid, and muscle was significantly different (P < .001), allowing for differentiation between these tissues. CONCLUSION: The electrical impedance spectra of rabbit thyroid and parathyroid glands are distinct and different from each other and from skeletal muscle. If these results are replicated in human tissue, they have the potential to improve patient outcomes by achieving early identification and preservation of parathyroid glands.

Use of electrical impedance spectroscopy to detect malignant and potentially malignant oral lesions.

The electrical properties of tissues depend on their architecture and cellular composition. We have previously shown that changes in electrical impedance can be used to differentiate between different degrees of cervical dysplasia and cancer of the cervix. In this proof-of-concept study, we aimed to determine whether electrical impedance spectroscopy (EIS) could distinguish between normal oral mucosa; benign, potentially malignant lesions (PML); and oral cancer. EIS data were collected from oral cancer (n=10), PML (n=27), and benign (n=10) lesions. EIS from lesions was compared with the EIS reading from the normal mucosa on the contralateral side of the mouth or with reference spectra from mucosal sites of control subjects (n=51). Healthy controls displayed significant differences in the EIS obtained from different oral sites. In addition, there were significant differences in the EIS of cancer and high-risk PML versus low-risk PML and controls. There was no significant difference between benign lesions and normal controls. Study subjects also deemed the EIS procedure considerably less painful and more convenient than the scalpel biopsy procedure. EIS shows promise at distinguishing among malignant, PML, and normal oral mucosa and has the potential to be developed into a clinical diagnostic tool.