We discuss recent advances in AI-assisted breast cancer risk prediction, what this means for the future of breast cancer screening and prevention, and the key research needed to progress mammographic features from research into clinical practice says senior study author Erik Thompson of the Queensland University of Technology in Brisbane, Australia.

Breast tissue that appears white on a mammogram is radiologically dense, while breast tissue that appears dark is considered non-dense. It is widely accepted that women with higher mammographic density for their age and body-mass index have a greater risk of breast cancer. In addition, higher density makes breast cancer harder to detect by mammography, known as the masking effect.

To predict a future breast cancer diagnosis, advanced computational approaches such as deep learning are now being used to analyze mammographic images.

In particular, AI methods are uncovering mammographic features that have the potential to be stronger predictors of breast cancer risk than any other known risk factor. These features might explain a large proportion of the association between mammographic density and breast cancer risk.

The discovery of the risk-predicting AI-generated mammographic features is providing new opportunities to identify women at most risk of developing breast cancer in the future and separating them from those women most at risk of having a breast cancer missed due to the masking effect.

Research suggests that some AI-generated mammographic features are indicative of early malignancy that is undetectable by radiologist-read mammography, while others may be benign conditions associated with an increased risk of breast cancer. The identity of AI-generated mammographic features that are not identified as cancer or a benign condition remains unclear.

Source: https://medicalxpress.com/news/2024-12-ai-mammography-breast-cancer.html