Prediction of Cancer Masking in Screening Mammography Using Density and Textural Features.

Rationale And Objectives: High mammographic density reduces the diagnostic accuracy of screening mammography due to masking of tumors, resulting in possible delayed diagnosis and missed cancers. Women with high masking risk could be preselected for alternative screening regimens less susceptible to masking. In this study, various models to predict masking status are presented based on biometric and image-based parameters.

Materials And Methods: For a cohort of 67 nonscreen-detected (cancers detected via other means after a negative mammogram) and 147 screen-detected invasive cancers, quantitative volumetric breast density, BI-RADS density, and the distribution and appearance of dense tissue through statistical and texture metrics were measured. Age and Body Mass Index were recorded. Stepwise multivariate logistic regressions were computed to select those parameters that predicted nonscreen-detected cancers. Accuracy of the models was evaluated using the area under receiver operator characteristic curve (AUC).

Results: Using BI-RADS density alone to predict masking risk yielded an AUC of 0.64 (95% confidence interval [0.57-0.70]). Age-adjusted BI-RADS density or volumetric breast density had AUCs of 0.72 [0.64-0.79] and 0.71 [0.62-0.78], respectively. A model extracted from the full pool of variables had an AUC of 0.75 [0.67-0.82].

Conclusion: The optimal model predicts masking more accurately than density alone, suggesting that texture metrics may be useful in models to guide a stratified screening strategy.