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Papers Published

1. Baker, J.A. and Kornguth, P.J. and Lo, J.Y. and Floyd, C.E., Artificial neural network: improving the quality of breast biopsy recommendations, Radiology (USA), vol. 198 no. 1 (1996), pp. 131 - 5 .
   (last updated on 2007/04/15)

   Abstract:
   The authors evaluated the performance and inter- and intraobserver variability of an artificial neural network (ANN) for predicting breast biopsy outcome. Five radiologists described 60 mammographically detected lesions with the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) nomenclature. A previously programmed ANN used the BI-RADS descriptors and patient histories to predict biopsy results. ANN predictive performance was compared with the clinical decision to perform biopsy. Interand intraobserver variability of radiologists interpretations and ANN predictions were evaluated with Cohen κ analysis. The ANN maintained 100% sensitivity (23 of 23 cancers) while improving the positive predictive 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). Interobserver variability for interpretation of the lesions was significantly reduced by the ANN (P<.001); there was no statistically significant effect on nearly perfect intraobserver reproducibility. It is concluded that use of an ANN with radiologists descriptions of abnormal findings may improve interpretation of mammographic abnormalities

   Keywords:
   diagnostic radiography;neural nets;