Factors affecting the rate of false positive marks in CAD in full-field digital mammography.

OBJECTIVE

To assess the effect of breast density, fibroglandular tissue volume, and breast volume on the rate of false-positive marks of a computer-assisted detection software in digital mammography.

MATERIALS AND METHODS

222 patients with normal digital mammograms and a minimum follow-up of 22 months were retrospectively identified. MLO and CC views were analyzed using a CAD software with three operating points ('specific', 'balanced', 'sensitive'). False-positive marks were recorded. Images were analyzed by a volumetric breast density assessment software, yielding estimates of percentage density, fibroglandular tissue volume, and breast volume. Statistical analysis was performed using the Mann-Whitney U-test, the t-test for independent samples and the Poisson regression model.

RESULTS

Patients with high fibroglandular tissue volumes had a higher mean number of false-positive mass marks than patients with low fibroglandular tissue volumes (specific setting: 0.50 vs. 0.35, respectively; balanced setting: 0.70 vs. 0.40, respectively, p<0.05; sensitive setting: 0.89 vs. 0.58, respectively, p<0.05). Relative risk for a false-positive mass marker increased by 1.43 (p<0.05), 1.63 (p<0.001) and 1.50 (p<0.01) per 100ml of fibroglandular tissue for the specific, balanced and sensitive settings, respectively. No significant effects of percentage density or breast volume on the number or the relative risk of false-positive mass marks were observed.

CONCLUSION

The volume of fibroglandular tissue present, but not the percentage density of the breast, affected the specificity for masses of the CAD software investigated. This may have implications for improving the performance of CAD systems, as the specificity of CAD may be improved by adjusting the algorithm threshold depending on the volume of fibroglandular tissue present. Considering both factors, fibroglandular tissue volume and percentage density, independently, could improve overall CAD performance in subgroups of patients, e.g. those with small, dense breasts or large breasts with low density.