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Proceedings Paper

Classification of thyroid nodules using a resonance-frequency-based electrical impedance spectroscopy: progress assessment
Author(s): Bin Zheng; Mitchell E. Tublin; Dror Lederman; Amy H. Klym; Erica D. Brown; David Gur
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Paper Abstract

The incidence of thyroid cancer is rising faster than other malignancies and has nearly doubled in the United States (U.S.) in the last 30 years. However, classifying between malignant and benign thyroid nodules is often difficult. Although ultrasound guided Fine Needle Aspiration Biopsy (FNAB) is considered an excellent tool for triaging patients, up to 25% of FNABs are inconclusive. As a result, definitive diagnosis requires an exploratory surgery and a large number of these are performed in the U.S. annually. It would be extremely beneficial to develop a non-invasive tool or procedure that could assist in assessing the likelihood of malignancy of otherwise indeterminate thyroid nodules, thereby reducing the number of exploratory thyroidectomies that are performed under general anesthesia. In this preliminary study we demonstrate a unique hand-held Resonance-frequency based Electrical Impedance Spectroscopy (REIS) device with six pairs of detection probes to detect and classify thyroid nodules using multi-channel EIS output signal sweeps. Under an Institutional Review Board (IRB)-approved case collection protocol, this REIS device is being tested in our clinical facility and we have been collecting an initial patient data set since March of this year. Between March and August of 2011, 65 EIS tests were conducted on 65 patients. Among these cases, six depicted pathology-verified malignant cells. Our initial assessment indicates the feasibility of easily applying this REIS device and measurement approach in a very busy clinical setting. The measured resonance frequency differences between malignant and benign nodules could potentially make it possible to accurately classify indeterminate thyroid nodules.

Paper Details

Date Published: 22 February 2012
PDF: 7 pages
Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181G (22 February 2012); doi: 10.1117/12.911553
Show Author Affiliations
Bin Zheng, Univ. of Pittsburgh (United States)
Mitchell E. Tublin, Univ. of Pittsburgh (United States)
Dror Lederman, Univ. of Pittsburgh (United States)
Amy H. Klym, Univ. of Pittsburgh (United States)
Erica D. Brown, Univ. of Pittsburgh (United States)
David Gur, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 8318:
Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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