[An improved method for determining the mean parenchyma dose in mammography]

Area of Science:

• Medical Physics
• Radiology
• Oncology

Background:

• Current mammography risk assessment relies on mean glandular dose (DG).
• DG is typically calculated using standard tissue composition (50% glandular/50% adipose) and Monte Carlo simulations.
• Accurate DG calculation is crucial for characterizing carcinogenesis risk.

Purpose of the Study:

• To develop and validate a method for more precise calculation of mean glandular dose (DG) in mammography.
• To incorporate individual breast tissue composition into DG calculations for improved accuracy.
• To compare the novel method with conventional DG calculation techniques.

Main Methods:

• Utilized signals from the mammographic automatic exposure control (AEC) double-detector to determine mean breast tissue composition.
• Calculated breast-specific conversion factors based on determined tissue composition.
• Applied the new method to 1020 women and compared results with conventional DG calculations.

Main Results:

• The novel method, using tissue-specific conversion factors, demonstrated improved accuracy in DG calculation.
• Average deviations in mean glandular dose (DG) were found to be 9% compared to conventional methods.
• In specific cases, differences in DG reached up to 35%, highlighting the impact of tissue composition.

Conclusions:

• Incorporating individual breast tissue composition significantly refines mean glandular dose (DG) estimation in mammography.
• This personalized approach offers a more accurate basis for assessing breast cancer carcinogenesis risk.
• The findings support the adoption of tissue-composition-adapted conversion factors for routine mammography dose assessment.