Analysis of mammographic findings and patient history data with genetic algorithms for the prediction of breast cancer biopsy outcome

Erik D. Frederick; Carey E. Floyd Jr.

doi:10.1117/12.310897

24 June 1998 Analysis of mammographic findings and patient history data with genetic algorithms for the prediction of breast cancer biopsy outcome

Erik D. Frederick, Carey E. Floyd Jr.

Author Affiliations +

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310897
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

A decision model is presented to increase the specificity of breast biopsy directly optimized on the receiver operating characteristic (ROC) area index. ROC area has higher clinical significance as a performance measure than the traditional metric mean-squared error (MSE). Excisional biopsy as practiced is highly sensitive to cancer but nonspecific; only one in three biopsies is malignant. Data for this study consists of 500 cases randomly selected from patients who underwent excisional biopsy for definitive diagnosis of breast cancer. For each case, inputs to the model consist of mammographic findings and patient history features. Outputs from the model built may be thresholded to correspond to the decision to biopsy a suspicious breast lesion. While clinically relevant, ROC area is a discontinuous function which cannot be optimized directly so a genetic algorithm approach is used to train a nonlinear artificial neural network. Performance using the genetic algorithm method of training was similar to that of a decision model trained using the traditional approach for this data set. ROC areas were obtained after training using three different approaches: genetic algorithm training optimized on ROC area produced an ROC area of 0.845 +/- 0.039, genetic algorithm training optimized on MSE produced an ROC area of 0.845 +/- 0.039, and traditional training using backpropagation produced an ROC area of 0.848 +/- 0.039. Despite the similar performance measures for models trained on this data, it is possible that with different data sets, training on ROC instead of MSE will produce models with significantly different performance. In this case, the genetic algorithm approach will prove useful.

Citation Download Citation

Erik D. Frederick and Carey E. Floyd Jr. "Analysis of mammographic findings and patient history data with genetic algorithms for the prediction of breast cancer biopsy outcome", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310897

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