PREDICTING BREAST-CANCER INVASION WITH ARTIFICIAL NEURAL NETWORKS ON THE BASIS OF MAMMOGRAPHIC FEATURES

PURPOSE: To evaluate whether an artificial neural network (ANN) can pr edict breast cancer invasion on the basis of readily available medical findings (ie, mammographic findings classified according to the Ameri can College of Radiology Breast Imaging Reporting and Data System and patient age). MATERIALS AND METHODS: In 254 adult patients, 266 lesion s that had been sampled at biopsy were randomly selected for the study . There were 96 malignant and 170 benign lesions. On the basis of nine mammographic findings and patient age, a three-layer backpropagation network was developed to predict whether the malignant lesions were in situ or invasive. RESULTS: The ANN predicted invasion among malignant lesions with an area under the receiver operating characteristic curv e (A(z)) of .91 +/- .03. It correctly identified all 28 in situ cancer s (specificity, 100%) and 48 of 68 invasive cancers (sensitivity, 71%) . CONCLUSION: The ANN used mammographic features and patient age to ac curately classify invasion among breast cancers, information that was previously available only by means of biopsy. This knowledge may assis t in surgical planning and may help reduce the cost and morbidity of u nnecessary biopsy.