Currently, mammography is the only population based breast cancer screening modality. In order to improve efficacy of mammography and increase cancer detection yield, it has been recently attracting extensive research interest to identify new mammographic imaging markers and/or develop novel machine learning models to more accurately assess or predict short-term breast cancer risk. Objective of this study is to explore and test a new quantitative image marker based on the analysis of frequency domain correlation based features between the bilateral asymmetry of image characteristics to predict risk of women having or developing mammography detectable cancer in a short-term. For this purpose, we assembled an image dataset involving 1,042 sets of “prior” negative mammograms. In the next subsequent “current” mammography screening, 402 cases were positive with cancer detected and verified, while 642 cases remained negative. A special computer-aided detection (CAD) scheme was applied to pre-process two bilateral mammograms of the left and right breasts, generate image maps in frequency domain, compute image features, and apply a multi-feature fusion based support vector machine based classifier to predict short-term breast cancer risk. By using a 10-fold crossvalidation method, this CAD based risk model yielded a performance of AUC = 0.72±0.04 (area under a ROC curve) and an odds ratio of 5.92 with 95% confidence interval of [4.32, 8.11]. This study presented a new type of mammographic imaging marker or a machine learning prediction model and demonstrated its feasibility to help predict short-term risk of developing breast cancer using a large and diverse image dataset.