A computer-aided diagnosis (CADx ) framework for the diagnosis of clustered microcalcifications (MCs) has already been developed, which is based on the analysis of MCs' morphologies,the shape of the cluster they form and the texture of the surrounding tissue. In this study, we investigate the diagnostic information that the relative location of the cluster inside the breast may provide. Breast probabilistic maps are generated and adopted in the CADx pipeline, expecting to empower its diagnostic procedure. We propose a flowchart combining alternative classification algorithms and the aforementioned probabilistic maps in order to provide a final risk for malignancy for new considered mammograms. For the evaluation performance, a large dataset of mammograms provided from the Digital Database of Screening Mammography (DDSM) has been used. The obtained results indicate that the proposed modifications lead to the enhancement of the diagnostic process, as the classification results are further improved. Additionally, a straightforward comparison between the CADx pipeline and the radiologists who assessed the same mammograms, reveal that the CADx pipeline performs toward the right direction, as the sensitivity remains at high levels, while improving both the accuracy, from 51.4% to 69%, and the specificity, from 16.6% to 54.7%.