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Proceedings Paper

A support vector machine designed to identify breasts at high risk using multi-probe generated REIS signals: a preliminary assessment
Author(s): David Gur; Bin Zheng; Dror Lederman; Sreeram Dhurjaty; Jules Sumkin; Margarita Zuley
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Paper Abstract

A new resonance-frequency based electronic impedance spectroscopy (REIS) system with multi-probes, including one central probe and six external probes that are designed to contact the breast skin in a circular form with a radius of 60 millimeters to the central ("nipple") probe, has been assembled and installed in our breast imaging facility. We are conducting a prospective clinical study to test the performance of this REIS system in identifying younger women (< 50 years old) at higher risk for having or developing breast cancer. In this preliminary analysis, we selected a subset of 100 examinations. Among these, 50 examinations were recommended for a biopsy due to detection of a highly suspicious breast lesion and 50 were determined negative during mammography screening. REIS output signal sweeps that we used to compute an initial feature included both amplitude and phase information representing differences between corresponding (matched) EIS signal values acquired from the left and right breasts. A genetic algorithm was applied to reduce the feature set and optimize a support vector machine (SVM) to classify the REIS examinations into "biopsy recommended" and "non-biopsy" recommended groups. Using the leave-one-case-out testing method, the classification performance as measured by the area under the receiver operating characteristic (ROC) curve was 0.816 ± 0.042. This pilot analysis suggests that the new multi-probe-based REIS system could potentially be used as a risk stratification tool to identify pre-screened young women who are at higher risk of having or developing breast cancer.

Paper Details

Date Published: 23 February 2010
PDF: 10 pages
Proc. SPIE 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, 76271B (23 February 2010); doi: 10.1117/12.844452
Show Author Affiliations
David Gur, Univ. of Pittsburgh (United States)
Bin Zheng, Univ. of Pittsburgh (United States)
Dror Lederman, Univ. of Pittsburgh (United States)
Sreeram Dhurjaty, Dhurjaty Electronics Consulting LLC (United States)
Jules Sumkin, Univ. of Pittsburgh (United States)
Margarita Zuley, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 7627:
Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment
David J. Manning; Craig K. Abbey, Editor(s)

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