Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment

Abstract

Objectives: To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database.

Methods: The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database.

Results: The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups.

Conclusion: This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.

Figure 1

An example of applying our computer-aided detection (CAD) scheme to four full-field digital mammogram (FFDM) images acquired from a 48-year-old female diagnosed with cancer. The left two columns show original FFDM images and the right two columns highlight three CAD-segmented suspicious mass regions, of which two are associated with a spiculated mass (right breast) and one is false positive (left breast).

Figure 2

Fraction of cases with a specific breast tissue density [Breast Imaging Reporting and Data System (BI-RADS)] rating in each of four categories of cases.

Figure 3

Three region-based normalised performance curves for the training set, testing set and the entire ensemble of suspected mass regions initially detected in the database.

Figure 4

Two free-response receiver operating characteristic-type computer-aided detection case-based performance curves for all current and prior images in the database.

Figure 5

Distribution of initially detected and computer-aided detection (CAD)-cued mass regions as a function of computed mass conspicuity.

Figure 6

Distribution of initially detected and computer-aided detection (CAD)-cued mass regions as a function of computed integrated density.