Since conventional computer-aided detection (CAD) schemes of mammograms produce high false positive detection rates, radiologists often ignore CAD-cued suspicious regions, in particular, the mass-type regions, which reduces the application value of CAD in clinical practice. The objective of this study is to investigate a new hypothesis that CAD-generated detection results may be useful and have a positive association to the mammographic cases with a high risk of being positive for cancer. To test this hypothesis, a large and diverse image dataset including mammograms acquired from 2,349 women was retrospectively assembled. Among them, 882 are positive and 1,467 are negative. Each case involves 4 images of craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breasts. A CAD scheme was applied to process all mammograms. From CAD results, a number of bilateral difference features from the matched CC and MLO view images of right and left breasts were computed. We analyzed discriminatory power to predict the risk of cases being positive using the bilateral difference features and a multi-feature fusion based Logistic-Regression machine learning classifier. By using a leave-onecase- out cross-validation method, the area under the ROC curve of the classifier for the multi-feature fusion was AUC=0.660 ±0.012. By applying an operating threshold at 0.5, the overall prediction accuracy was 67% and the odds ratio was 4.794 with a statistically significant increasing trend (p<0.01). Study results indicated that from CAD-generated false-positives, we enabled to generate a new quantitative imaging marker to predict higher risk cases being positive and cue a case-based warning sign.