Regional disparities in visual assessment of breast density: implications for risk stratification in breast cancer detection

High breast density is considered a risk factor for breast cancer, and it is particularly important for early detection when masses are small and difficult to see. The BI-RADS reporting system provides guidelines for standardized visual assessment of breast density. Nevertheless, such guidelines which rely in part on the delineation of dense tissues are likely to lead to variability between annotators. In this present study, we hypothesized that such variability is amplified when looking at density scores assigned by trained radiologists of different countries. The hypothesis was tested on a retrospectively collected dataset of mammography images which were assigned with a density value from 3 radiologists from France and 4 radiologists from the United States. In a further step, we used an AI-model to automatically assess density in all images, and compared predictions to annotations obtained from both countries. We then implemented a calibration procedure to adjust those predictions for regional effect. Comparing consensus-based labels between the French and the US datasets resulted in a significant difference. The proposed AI-based model, after undergoing a region-specific calibration procedure, was consistent with the expected behavior showing good agreement with French based or US based consensus respectively.