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Proceedings Paper

Assembling a prototype resonance electrical impedance spectroscopy system for breast tissue signal detection: preliminary assessment
Author(s): Jules Sumkin; Bin Zheng; Michelle Gruss; John Drescher; Joseph Leader; Walter Good; Amy Lu; Cathy Cohen; Ratan Shah; Margarita Zuley; David Gur
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Paper Abstract

Using electrical impedance spectroscopy (EIS) technology to detect breast abnormalities in general and cancer in particular has been attracting research interests for decades. Large clinical tests suggest that current EIS systems can achieve high specificity (≥ 90%) at a relatively low sensitivity ranging from 15% to 35%. In this study, we explore a new resonance frequency based electrical impedance spectroscopy (REIS) technology to measure breast tissue EIS signals in vivo, which aims to be more sensitive to small tissue changes. Through collaboration between our imaging research group and a commercial company, a unique prototype REIS system has been assembled and preliminary signal acquisition has commenced. This REIS system has two detection probes mounted in the two ends of a Y-shape support device with probe separation of 60 mm. During REIS measurement, one probe touches the nipple and the other touches to an outer point of the breast. The electronic system continuously generates sweeps of multi-frequency electrical pulses ranging from 100 to 4100 kHz. The maximum electric voltage and the current applied to the probes are 1.5V and 30mA, respectively. Once a "record" command is entered, multi-frequency sweeps are recorded every 12 seconds until the program receives a "stop recording" command. In our imaging center, we have collected REIS measurements from 150 women under an IRB approved protocol. The database includes 58 biopsy cases, 78 screening negative cases, and other "recalled" cases (for additional imaging procedures). We measured eight signal features from the effective REIS sweep of each breast. We applied a multi-feature based artificial neural network (ANN) to classify between "biopsy" and normal "non-biopsy" breasts. The ANN performance is evaluated using a leave-one-out validation method and ROC analysis. We conducted two experiments. The first experiment attempted to classify 58 "biopsy" breasts and 58 "non-biopsy" breasts acquired on 58 women each having one breast recommended for biopsy. The second experiment attempted to classify 58 "biopsy" breasts and 58 negative breasts from the set of screening negative cases. The areas under ROC curves are 0.679 ± 0.033 and 0.606 ± 0.035 for the first and the second experiment, respectively. The preliminary results demonstrate (1) even with this rudimentary system with only one paired probes there is a measurable signal of changes in breast tissue demonstrating the feasibility of applying REIS technology for identifying at least some women with highly suspicious breast abnormalities and (2) the electromagnetic asymmetry between two breasts may be more sensitive in detecting changes in the abnormal breast. To further improve the REIS system performance, we are currently designing a new REIS system with multiple electrical probes and a more sophisticated analysis scheme.

Paper Details

Date Published: 6 March 2008
PDF: 8 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691716 (6 March 2008); doi: 10.1117/12.770457
Show Author Affiliations
Jules Sumkin, Univ. of Pittsburgh (United States)
Bin Zheng, Univ. of Pittsburgh (United States)
Michelle Gruss, Univ. of Pittsburgh (United States)
John Drescher, Univ. of Pittsburgh (United States)
Joseph Leader, Univ. of Pittsburgh (United States)
Walter Good, Univ. of Pittsburgh (United States)
Amy Lu, Univ. of Pittsburgh (United States)
Cathy Cohen, Univ. of Pittsburgh (United States)
Ratan Shah, Univ. of Pittsburgh (United States)
Margarita Zuley, Univ. of Pittsburgh (United States)
David Gur, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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