Risk Stratification for Breast Cancer Screening: AJR Expert Panel Narrative Review

Area of Science:

• Radiology and Oncologic Imaging
• Medical Informatics
• Public Health

Background:

• Early breast cancer detection significantly reduces mortality.
• Screening effectiveness is enhanced by aligning strategies with individual cancer risk profiles.
• Risk prediction tools aid in assessing patient-specific cancer risks.

Purpose of the Study:

• To review breast cancer risk stratification methods and prediction models.
• To analyze current societal guidelines on risk assessment tool utilization.
• To propose a practical framework for integrating risk assessment into clinical practice.

Main Methods:

• Narrative review of breast cancer risk stratification.
• Overview of commonly used clinical risk prediction models.
• Analysis of existing professional society guidelines.

Main Results:

• Variability exists in professional society recommendations for risk assessment.
• Uncertainty persists regarding standardized risk assessment approaches and tool implementation.
• Deep learning models show potential for future risk stratification.

Conclusions:

• Aligning screening with individual risk profiles improves benefit-harm balance.
• Standardized risk assessment and clear guidelines are needed for effective implementation.
• Future research, including deep learning, can refine breast cancer screening strategies.