Artificial neural network: improving the quality of breast biopsy recommendations.
OBJECTIVE
To evaluate the performance and inter- and intraobserver variability of an artificial neural network (ANN) for predicting breast biopsy outcome.


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.


CONCLUSIONS
Use of an ANN with radiologists' descriptions of abnormal findings may improve interpretation of mammographic abnormalities.
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
J A
JA
Baker
P J
PJ
Kornguth
J Y
JY
Lo
C E
CE
Floyd
D001706:Biopsy; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity