Computer-aided detection system applied to full-field digital mammograms

Abstract

Background: Although mammography remains the mainstay for breast cancer screening, it is an imperfect examination with a sensitivity of 75–92% for breast cancer. Computer-aided detection (CAD) has been developed to improve mammographic detection of breast cancer. Purpose: To retrospectively estimate CAD sensitivity and false-positive rate with full-field digital mammograms (FFDMs). Material and Methods: CAD was used to evaluate 151 cases of ductal carcinoma in situ (DCIS) ( n=48) and invasive breast cancer ( n=103) detected with FFDM. Retrospectively, CAD sensitivity was estimated based on breast density, mammographic presentation, histopathology type, and lesion size. CAD false-positive rate was estimated with screening FFDMs from 200 women. Results: CAD detected 93% (141/151) of cancer cases: 97% (28/29) in fatty breasts, 94% (81/86) in breasts containing scattered fibroglandular densities, 90% (28/31) in heterogeneously dense breasts, and 80% (4/5) in extremely dense breasts. CAD detected 98% (54/55) of cancers manifesting as calcifications, 89% (74/83) as masses, and 100% (13/13) as mixed masses and calcifications. CAD detected 92% (73/79) of invasive ductal carcinomas, 89% (8/9) of invasive lobular carcinomas, 93% (14/15) of other invasive carcinomas, and 96% (46/48) of DCIS. CAD sensitivity for cancers 1–10 mm was 87% (47/54); 11–20 mm, 99% (70/71); 21–30 mm, 86% (12/14); and larger than 30 mm, 100% (12/12). The CAD false-positive rate was 2.5 marks per case. Conclusion: CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications or masses. CAD sensitivity was maintained in small lesions (1–20 mm) and invasive lobular carcinomas, which have lower mammographic sensitivity.