Quantifying masking in clinical mammograms via local detectability of simulated lesions

High mammographic density is known to be associated with decreased sensitivity of mammography. Recent changes in the BI-RADS density assessment address the effect of masking by densities, but the BI-RADS assessment remains qualitative and achieves only moderate agreement between radiologists. An automated, quantitative algorithm that estimates the likelihood of masking of simulated masses in a mammogram by dense tissue has been developed. The algorithm considers both the effects of loss of contrast due to density and the distracting texture or appearance of dense tissue.

A local detectability (dL) map is created by tessellating the mammograms into overlapping regions of interest (ROIs), for which the detectability by a non-prewhitening observer is computed using local estimates of the noise power spectrum and volumetric breast density (VBD). The dL calculation was validated in a 4-alternative forced-choice observer study on the ROIs of 150 craniocaudal digital mammograms. The dL metric was compared against the inverse threshold contrast, (ΔμT)(-1) from the observer study, the anatomic noise parameter β, the radiologist's BI-RADS density category, and a validated measure of VBD (Cumulus).

The mean dL had a high correlation of r = 0.915 and r = 0.699 with (ΔμT)(-1) in the computerized and human observer study, respectively. In comparison, the local VBD estimate had a low correlation of 0.538 with (ΔμT)(-1). The mean dL had a correlation of 0.663, 0.835, and 0.696 with BI-RADS density, β, and Cumulus VBD, respectively.

The proposed dL metric may be useful in characterizing the potential for lesion masking by dense tissue. Because it uses information about the anatomic noise or tissue appearance, it is more closely linked to lesion detectability than VBD metrics.