Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.
OBJECTIVE
To determine if an artificial neural network (ANN) to categorize benign and malignant breast lesions can be standardized for use by all radiologists.


METHODS
An ANN was constructed based on the standardized lexicon of the Breast Imaging Recording and Data System (BI-RADS) of the American College of Radiology. Eighteen inputs to the network included 10 BI-RADS lesion descriptors and eight input values from the patient's medical history. The network was trained and tested on 206 cases (133 benign, 73 malignant cases). Receiver operating characteristic curves for the network and radiologists were compared.


RESULTS
At a specified output threshold, the ANN would have improved the positive predictive value (PPV) of biopsy from 35% to 61% with a relative sensitivity of 100%. At a fixed sensitivity of 95%, the specificity of the ANN (62%) was significantly greater than the specificity of radiologists (30%) (P < .01).


CONCLUSIONS
The BI-RADS lexicon provides a standardized language between mammographers and an ANN that can improve the PPV of breast biopsy.
Baker
J A
JA
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
Kornguth
P J
PJ
Lo
J Y
JY
Williford
M E
ME
Floyd
C E
CE
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001706:Biopsy; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D011871:Radiology; D012680:Sensitivity and Specificity; D009626:Terminology as Topic