Recently, the generalized additive models (GAMs) have been presented as a novel statistical approach to distinguish lesion/non-lesion in computer-aided diagnosis (CAD) systems. In this paper, we present an extension of the GAM that allows for the introduction of factors and their interactions with continuous variables, for reducing false positives in a CAD system for detecting clustered microcalcifications in digital mammograms. The results obtained have shown an increase in the sensitivity from 83.12% to 85.71%, while the false positive rate was drastically reduced from 1.46 to 0.74 false detections per image.