Verification of lesion correspondence in images from different modalities is an essential but nontrivial step in the multi-modality computer-aided diagnosis (CADx) of breast cancer. This study extends the correlative feature analysis (CFA) method to lesions that are imaged by both full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and incorporates the CFA method into a lesion characterization procedure. Computerized algorithms are firstly applied to segment lesions and extract features in images. For each pair-wise feature, a Bayesian artificial neural network (BANN) is employed to yield a correspondence score. A subset of correspondence scores is then automatically selected and merged by another BANN to yield an overall probability of correspondence (PC). Depending on the overall PC value, the probability of malignancy of a lesion is estimated by either a single-modality CADx or multi-modality CADx. Receiver operating characteristic (ROC) analysis is used to evaluate the performance of lesion correspondence identification and lesion classification. In the task of identifying corresponding lesions in images from different modalities, the PC values in the MRI vs. craniocaudal (CC) category yielded an area under the curve (AUC) of 0.78 ± 0.03, those in the MRI vs. mediolateral oblique (MLO) category yielded an AUC of 0.86 ± 0.03, and those in the MRI vs. mediolateral (ML) category yielded an AUC of 0.84 ± 0.02. The use of multiple feature-based correspondence scores statistically improves performance compared to the use of a single correspondence score (overall α = 0.05). With CFA included in the task of distinguishing malignant lesions from benign lesions, the proposed scheme yielded an AUC of 0.84 ± 0.03 for lesions imaged with MR, and an AUC of 0.82 ± 0.03 for lesions imaged with FFDM. The results of this pilot study indicate that multi-modality CADx with CFA has the potential to provide improved diagnostic accuracy.