A model-based method for reporting mammographic diagnostic reference levels for any compressed breast thickness: A refined reporting approach

Background: Mammography is a critical tool for early breast cancer detection, but its use of ionizing radiation necessitates careful monitoring and optimization of patient exposure to ensure safety. Conventional methods for reporting diagnostic reference levels (DRLs) rely on wide compressed breast thickness (CBT) ranges, which lack the precision to account for individual variations, limiting their effectiveness in optimizing mammographic radiation doses. Purpose: To develop an equation-based approach that provides a DRL for any given CBT. Methods: The 75th percentile of median average glandular dose (AGD) values from nine centers (a total of 187,704 mammograms) was employed for the DRL estimation using three approaches: (1) estimating a DRL for a CBT range assumed to represent typical women in the population (simplest/common approach), (2) reporting DRLs per different 10-mm CBT ranges (improved approach), and (3) as a DRL equation (the proposed approach), which was generated from fitting the values of the estimated DRLs versus each corresponding CBT. The differences in these approaches’ results were compared. Results: For our population, the curve fitting of the DRLs versus their corresponding CBTs resulted in a bi-exponential equation with high-fitting reliability (R² > 0.98). The equation approach provides continuous DRL values to serve any given CBT. The difference in the estimated DRLs by the equation approach and the improved range-based reporting approach can range between several percentages to more than 35% and can exceed that to more than 175% when compared with the estimated simplest approach's DRL. Conclusions: The proposed DRL equation approach is reliable and can be used to provide more precise results for any given CBT value rather than the conventional range-based reporting approaches. Vendors can adopt the proposed approach by integrating an option to input DRL equations to automate the optimization of mammographic dose.