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Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.

Nazli A Moda1, Mo'ayyad E Suleiman1, Sahand Hooshmand1

  • 1Faculty of Medicine and Health, Discipline of Medical Imaging Sciences, The University of Sydney, Susan Wakil Health Building (D18), Sydney, Australia.

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Summary

This review examines models for radiation risk from mammography screening. While risks are low, factors like breast density and AI tools impact dose estimation, needing further development for consistent application.

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Area of Science:

  • Radiology
  • Medical Physics
  • Oncology

Background:

  • Mammography screening is vital for breast cancer detection but raises concerns about radiation exposure.
  • Understanding long-term radiation risks is crucial to address participation barriers.

Purpose of the Study:

  • To explore and synthesize existing models that estimate long-term radiation risks from repeated mammography screening.
  • To identify factors influencing radiation dose and the role of artificial intelligence in dose and breast density estimation.

Main Methods:

  • A scoping review methodology was employed, searching five major databases and conducting manual searches.
  • Included 24 studies published between 2014 and 2024, categorizing findings into dose-risk profiles, dose-influencing factors, and AI applications.

Main Results:

  • Breast density, compressed breast thickness, and imaging parameters significantly affect mean glandular dose (MGD).
  • Modeling studies indicate a low risk of radiation-induced cancer, but highlight protocol inconsistencies and vendor limitations.
  • Artificial intelligence shows promise for personalized dose-risk assessments but requires further development for cross-platform compatibility.

Conclusions:

  • Current models suggest low radiation-induced cancer risk from mammography, though protocol standardization is needed.
  • Artificial intelligence offers potential for improved individual risk assessment, but requires enhanced interoperability.
  • Further research is essential to refine dose estimation models and integrate AI effectively into mammography screening.