ARTIFICIAL NEURAL-NETWORK - IMPROVING THE QUALITY OF BREAST BIOPSY RECOMMENDATIONS

PURPOSE: To evaluate the performance and inter- and intraobserver vari ability of an artificial neural network (ANN) for predicting breast bi opsy outcome. MATERIALS AND METHODS: Five radiologists described 60 ma mmographically detected lesions with the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) nomenclature. A pr eviously programmed ANN used the BI-RADS descriptors and patient histo ries to predict biopsy results. ANN predictive performance was compare d with the clinical decision to perform biopsy. Inter- and intraobserv er variability of radiologists' interpretations and ANN predictions we re evaluated with Cohen kappa analysis. RESULTS: The ANN maintained 10 0% sensitivity (23 of 23 cancers) while improving the positive predict ive value of biopsy results from 38% (23 of 60 lesions) to between 58% (23 of 40 lesions) and 66% (23 of 35 lesions; P < .001). Interobserve r variability for interpretation of the lesions was significantly redu ced by the ANN (P < .001); there was no statistically significant effe ct on nearly perfect intraobserver reproducibility. CONCLUSION: Use of an ANN with radiologists' descriptions of abnormal findings may impro ve interpretation of mammographic abnormalities.