Use of BI-RADS lesion descriptors in computer-aided diagnosis of malignant and benign breast lesions

The purpose of this study was to determine whether combining an automated computer technique that classifies calcifications in mammograms as malignant or benign with radiologist-provided BI-RADS lesion description improves classification performance. Three expert mammography radiologists who were MQSA certified and familiar with BI-RADS retrospectively interpreted 125 cases of mammograms containing calcifications and provided BI-RADS lesion descriptions. A computer technique was applied to the mammograms to extract eight image features that describe the size, shape, and uniformity of individual as well as groups of calcifications. We compared the performance of artificial neural networks that estimated the likelihood of malignancy based on input from either the computer-extracted image features alone, the BI-RADS lesion descriptors alone, or the combination of both. The leave-one-out method was used. Combining the BI-RADS lesion description provided by a single radiologist and computer-extracted image features resulted in improved performance. However, using two radiologists' BI-RADS lesion descriptions such that one radiologist's data was used to train and another radiologist's data was used to test the neural network diminished this improvement in performance. These results suggest that variability in radiologists' BI-RADS lesion description is large enough to offset a potential gain in performance from combining it with an automated computer technique.