A new publication by InforMD members has highlighted the potential for the use of artificial intelligence (AI) in identifying women with increased breast cancer risk. The opinion piece, published in Trends in Cancer, explores how AI can help clinicians to better identify features on a mammogram that indicate a high risk of developing breast cancer.
The patterns of white and dark on a mammogram have long been studied as mammographic breast density, which is a known risk factor for breast cancer. "It’s within these patterns of mammographic density that AI is now finding new mammographic features that can be used to identify those women most at risk of a future breast cancer diagnosis," said InforMD member Associate Professor Wendy Ingman and lead author of the paper.
InforMD member Professor Rik Thompson, senior author of the paper, said “There are a growing number of studies from Australia and internationally suggesting that AI-generated mammographic features are indicative of early malignancy, undetectable by radiologists, but may also represent benign conditions like atypical ductal hyperplasia, which is associated with an increased risk of breast cancer,” said Professor Rik Thompson.
“Certain mammographic features could be areas of high pathological activity that increases the chance of cancer developing,” said Professor Thompson. “Critically, we need to identify the pathobiology associated with mammographic features and the underlying mechanisms that link them with development of breast cancer. It is this common goal that brings us together.”
Associate Professor Helen Frazer, a breast radiologist leading research studies that investigate use of AI-generated risk-scores within the BreastScreen Victoria program, says research in this space could create new opportunities to improve breast cancer screening, tailored to suit individual needs. “Use of AI could help us identify those women at increased risk of developing breast cancer in the future and be a step forward in personalising screening to best suit the individual and improve outcomes,” said Associate Professor Frazer.
Gerda Evans, InforMD member and and Co-Chair of the Australian Breast Density Consumer Advisory Council, has been working side-by-side with researchers exploring how AI can help refine mammography-based risk prediction. “This is a great advance in predicting breast cancer risk, with potentially huge benefits for the community,” said Mrs Evans.
Associate Professor Ingman said mammographic density is still a valuable measure of risk at the time of a mammogram. “AI is enabling us to refine mammographic density as a risk factor, and hone in on particular features in a mammogram that are stronger risk predictors, however high mammographic density remains a significant breast cancer risk factor,” said Associate Professor Ingman.
Tragically, InforMD member and co-author of the paper passed away before the work was published. Professor John Hopper was passionate about the potential for AI-generated mammographic features to shape the future of breast cancer screening.
"With this work, we intend to continue John’s legacy,”" said Professor Thompson.
Reference: Artificial intelligence improves mammography-based breast cancer risk prediction Ingman et al Trends in Cancer 2024; 9 6: 808-814 published online December 12, 2024
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