QUALITY CONTROL OF MAMMOGRAPHY EXAMS: A LITERATURE REVIEW

Abstract

This literature review analyzed studies published between 2020 and 2025 on quality control in mammography, identifying 86 papers, of which 9 met the eligibility criteria. Findings show that the absence of quality protocols significantly reduces diagnostic sensitivity, while the adoption of international guidelines and standardized tools increases result reliability. Key strategies include the use of phantoms, automated image analysis software, and regular equipment maintenance. Factors such as artifacts, acquisition issues, inadequate processing, and the influence of the reading environment were also highlighted as contributors to poor image quality. The literature further emphasizes the importance of continuous radiologist training and the central role of medical physicists in coordinating quality programs. Finally, radiomics combined with machine learning emerges as a promising approach, offering greater objectivity, the ability to manage variability across systems, and strong potential for clinical application.