High mammographic density reduces the diagnostic accuracy of mammography by masking tumors, leading to interval cancers and late stage diagnosis. In this study, various models to predict masking risk are computed on a cohort of 90 interval or undiagnosed (“masked”) invasive cancers and 186 screen-detected invasive cancers, based on biometric (age and BMI) and image-based parameters (BI-RADS density, volumetric breast density (VBD) and detectability). Univariate logistic regressions were computed to predict masked cancers, and the accuracy of the regressions was evaluated using the area under receiver operator characteristic curve (AUC). The univariate AUC for BMI, age, BIRADS density, VBD and mean detectability were 0.61 [0.54-0.68], 0.65 [0.58-0.73], 0.67 [0.61–0.73], 0.72 [0.65-0.78] and 0.75 [0.68-0.81] respectively (95% confidence intervals are noted in the brackets). The models were applied to a set of 248 mammography exams from cancer-free women of the same population. A stratified screening model was tested by computing the fraction of disease-free women identified as masked (the recruitment rate) as a function of the fraction of masked cancers that were correctly identified. For BI-RADS densities 3 or 4 (4th edition), up to 60% of masked cancers could potentially be detected by supplemental tests, requiring 43% of women to be recruited for extra screening. Selecting by mean detectability would require a 29% recruitment rate for the same potential capture. Future work to develop multivariate masking risk predictors could yield more efficient stratified screening approaches for breast cancer detection.