Automated detection of breast cancer as an aid in the interpretation of screening MRI

PURPOSE: Computer aided detection (CADe) of suspect abnormalities in MRI may prevent reading errors in breast cancer screening of women at high risk. In this study, we evaluate the performance of a CADe system in detecting breast cancers missed in screening and compare this to the performance obtained on screen-detected cancers. METHODS AND MATERIALS: We collected DCE-MRI studies from 163 women participating in a high risk screening program. These data included 26 scans with screen-detected cancers, and 23 scans with 10 and 13 cancers that were retrospectively visible (BI-RADS 4/5) or minimally visible (BI-RADS 2/3) in prior MRI screening exams, but were reported to be normal. Cancers were detected at the following screening round. Furthermore, 114 normal scans with no sign of breast cancer were included. Lesions were annotated on the first post-contrast subtraction image. A CADe system was developed in-house. The detection performance was evaluated using free-response receiver operating characteristic and bootstrapping. A CADe finding was considered true positive when its center was inside a lesion annotation. The false-positive rate (FP/case) was determined on the normal cases. RESULTS: At 4 FP/case, the sensitivity for screen-detected lesions was 0.80 (95% confidence interval 0.62-0.96). For lesions that were visible or minimally visible in prior false-negative studies, the sensitivities were 0.69 (0.33-1.00) and 0.47 (0.18-0.75), respectively. CONCLUSION: The detection performance for missed cancers of a CADe system was almost as high as for screen-detected cancers. The integration of such a system in clinical practice might aid radiologists to avoid screening errors.