Breast cancer CADx based on BI-RADS™ descriptors from two mammographic views

Abstract

In this study we compared the performance of computer aided diagnosis (CADx) algorithms based on Breast Imaging Reporting And Data System (BI-RADS) descriptors from one or two views. To select cases for the study with different mediolateral (MLO) and craniocaudal (CC) view descriptors, we assessed the agreement in BI-RADS lesion descriptors, BI-RADS assessment, and subtlety ratings for 1626 cases from the Digital Database for Screening Mammogrpahy (DDSM) using kappa statistics. We used 115 mass caseswith different descriptors for the two views to design linear discriminant analysis (LDA) based CADx algorithms. The CADx algorithms used BI-RADS descriptors and patient age as features. Thealgorithms based on BI-RADS descriptors from both the views performed marginally betterthan algorithms based on BI-RADS descriptors from a single view. A system that averaged theresults of two classifiers trained separately on the MLO and CC views displayed the best performance (Az=0.920 +/- 0.027). Thus, some improvement in performance of BI-RADS based CADx algorithms may be achieved by combining information from two mammographic views.