With the introduction of Full Field Digital Mammography (FFDM) accurate automatic volumetric breast density (VBD) estimation has become possible. As VBD enables the design of features that incorporate 3D properties, these methods offer opportunities for computer aided detection schemes. In this study we use VBD to develop features that represent how well a segmented region resembles the projection of a spherical object. The idea behind this is that due to compression of the breast, glandular tissue is likely to be compressed to a disc like shape, whereas cancerous tissue, being more diffcult to compress, will retain its uncompressed shape. For each pixel in a segmented region we calculate the predicted dense tissue thickness assuming that the lesion has a spherical shape. The predicted thickness is then compared to the observed thickness by calculating the slope of a linear function relating the two. In addition we calculate the variance of the error of the fit. To evaluate the contribution of the developed VBD features to our CAD system we use an FFDM dataset consisting of 266 cases, of which 103 were biopsy proven malignant masses and 163 normals. It was found that compared to the false positives, a large fraction of the true positives has a slope close to 1.0 indicating that the true positives fit the modeled spheres best. When the VBD based features were added to our CAD system, aimed at the detection and classiffcation of malignant masses, a small but signiffcant increase in performance was achieved.