The purpose was to evaluate the effect of incorporating negative but suspicious regions into a knowledge-based computer-aided detection (CAD) scheme of masses depicted in mammograms. To determine if a suspicious region is positive for a mass, the region was compared not only with actually positive regions (masses), but also with known negative regions. A set of quantitative measures (i.e., a positive, a negative, and a combined likelihood measure) was computed. In addition, a process was developed to integrate two likelihood measures that were derived using two selected features. An initial evaluation with 300 positive and 300 negative regions was performed to determine the parameters associated with the likelihood measures. Then, an independent set of 500 positive and 500 negative regions was used to test the performance of the CAD scheme. During the training phase, the performance was improved from Az=0.83 to 0.87 with the incorporation of negative regions and the integration process. During the independent test, the performance was improved from Az=0.80 to 0.83. The incorporation of negative regions and the integration process was found to add information to the scheme. Hence, it may offer a relatively robust solution to differentiate masses from normal tissue in mammograms.