A computer-aided diagnosis (CADx) system with the ability to predict the probability of malignancy (PM) of a mass can
potentially assist radiologists in making correct diagnostic decisions. In this study, we designed a CADx system using
logistic regression (LR) as the feature classifier which could estimate the PM of a mass. Our data set included 488
ultrasound (US) images from 250 biopsy-proven breast masses (100 malignant and 150 benign). The data set was
divided into two subsets T1 and T2. Two experienced radiologists, R1 and R2, independently provided Breast Imaging
Reporting and Data System (BI-RADS) assessments and PM ratings for data subsets T2 and T1, respectively. An LR
classifier was designed to estimate the PM of a mass using two-fold cross validation, in which the data subsets T1 and
T2 served once as the training and once as the test set. To evaluate the performance of the system, we compared the PM
estimated by the CADx system with radiologists' PM ratings (12-point scale) and BI-RADS assessments (6-point scale).
The correlation coefficients between the PM ratings estimated by the radiologists and by the CADx system were 0.71
and 0.72 for data subsets T1 and T2, respectively. For the BI-RADS assessments provided by the radiologists and
estimated by the CADx system, the correlation coefficients were 0.60 and 0.67 for data subsets T1 and T2, respectively.
Our results indicate that the CADx system may be able to provide not only a malignancy score, but also a more
quantitative estimate for the PM of a breast mass.