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Proceedings Paper

Computer-aided prediction of breast implant rupture based on mammographic findings
Author(s): Carey E. Floyd; Mary Scott Soo; Georgia D. Tourassi; Phyllis J. Kornguth
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Paper Abstract

A computer aided diagnostic system has been developed to predict the status of a breast implant (intact/ruptured) based on mammographic findings. Mammograms were obtained from 112 patients who presented for surgical removal of breast implants. Findings were recorded by radiologists for each patient. Of these 112 cases, 77 were ruptured while 35 were intact at the time of surgery. An artificial neural network (ANN) was trained to output the implant status when given the mammographic findings as inputs. The ANN was a backpropagation network with nine inputs, one hidden layer with 4 nodes, and one output node (implant status). The network was trained using the round-robin technique and evaluated using ROC analysis. The network performed well with an ROC area of 0.84. This was better than the radiologists's performance with sensitivity of 0.67 and specificity of 0.72. At a sensitivity of 0.67 (to match the radiologists), the network had a specificity of 0.89. At a specificity of 0.72 (to match the radiologists), the network has a sensitivity of 0.78. An ANN has been developed which demonstrates encouraging diagnostic performance for predicting the status of breast implants from mammographic findings.

Paper Details

Date Published: 12 May 1995
PDF: 7 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208718
Show Author Affiliations
Carey E. Floyd, Duke Univ. (United States)
Mary Scott Soo, Duke Univ. (United States)
Georgia D. Tourassi, Duke Univ. (United States)
Phyllis J. Kornguth, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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