Classification of Thyroid Nodules Using a Resonance-Frequency–Based Electrical Impedance Spectroscopy: A Preliminary Assessment

Abstract

Ultrasound and ultrasound-guided fine-needle aspiration biopsy are considered the most effective approaches for both identifying and classifying thyroid nodules. However, despite continuing improvements in scanner technology and refinements in ultrasound/cytological classification guidelines, indeterminate findings still lead to diagnostic lobectomy under general anesthesia. This study aims to investigate the feasibility of applying a modified noninvasive electrical impedance spectroscopy (EIS) approach to classifying thyroid nodules. To increase nodule classification sensitivity, we developed a new EIS-based model that introduces an optimized inductance component, which increases the measured signal-to-noise ratio of capacitance variation in and about thyroid nodules. Our model then measures the change of resonance frequency when the positive reactance of the system inductor cancels out the negative reactance of the nodule capacitance in a multi-frequency electrical signal scan. The system is termed "resonance-frequency-based electrical impedance spectroscopy" (REIS). A portable REIS system with multiple probes was assembled and preliminarily tested in our clinical facility. From an ongoing prospective study, an initial data set of 160 REIS examinations including 27 verified cancer cases was used. From the data set, a number of EIS signal features was extracted and analyzed. A multi-feature-based Bayesian Belief Network was built to classify the detected thyroid nodules. A receiver operating characteristic data analysis method was applied to evaluate classification performance. The results showed that (i) the median resonance frequency measured by the probe nearest to malignant nodules was in general lower than that measured in benign cases, and (ii) the median descending slope of EIS signal sweep curves computed from cancer cases was larger than that computed from benign cases. The Bayesian Belief Network yielded a classification performance as measured by the area under the receiver operating characteristic curve of 0.794 [with a 95% confidence interval of 0.709-0.863]. The study demonstrates that noninvasive measurement of REIS signal features may potentially provide useful supplementary information to assist in classifying between malignant and benign thyroid nodules. Such an approach may ultimately lead to a reduction in the number of unnecessary thyroid surgeries.