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Papers Published

1. JA Baker, PJ Kornguth, JY Lo, CE Floyd, Artificial neural network: improving the quality of breast biopsy recommendations., Radiology, UNITED STATES, vol. 198 no. 1 (January, 1996), pp. 131-5 .
   (last updated on 2006/02/06)

   Abstract:
   PURPOSE: To evaluate the performance and inter- and intraobserver variability of an artificial neural network (ANN) for predicting breast biopsy outcome. MATERIALS AND METHODS: 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. Inter- and intraobserver variability of radiologists' interpretations and ANN predictions were evaluated with Cohen kappa analysis. RESULTS: 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. CONCLUSION: Use of an ANN with radiologists' descriptions of abnormal findings may improve interpretation of mammographic abnormalities.

   Keywords:
   Biopsy* • Breast • Breast Neoplasms • Diagnosis, Computer-Assisted* • Female • Humans • Mammography • Middle Aged • Neural Networks (Computer)* • Observer Variation • Predictive Value of Tests • Sensitivity and Specificity • diagnosis* • pathology* • radiography