Computer-aided prediction of breast implant rupture based on mammographic findings

Carey E. Floyd Jr.; Mary Scott Soo; Georgia D. Tourassi; Phyllis J. Kornguth

doi:10.1117/12.208718

12 May 1995 Computer-aided prediction of breast implant rupture based on mammographic findings

Carey E. Floyd Jr., Mary Scott Soo, Georgia D. Tourassi, Phyllis J. Kornguth

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208718
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

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.

Citation Download Citation

Carey E. Floyd Jr., Mary Scott Soo, Georgia D. Tourassi, and Phyllis J. Kornguth "Computer-aided prediction of breast implant rupture based on mammographic findings", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208718

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