A recent publication by a national team of researchers has highlighted the potential of artificial intelligence (AI) in identifying women at higher risk of developing breast cancer.
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Published in Trends in Cancer, the study explores how AI can assist clinicians in spotting specific features on mammograms that signal an increased risk of developing the disease.
The research was led by Associate Professor Wendy Ingman from the University of Adelaide’s Robinson Research Institute, based at The Queen Elizabeth Hospital, with contributions from experts at QUT, the University of Melbourne, the Peter MacCallum Cancer Centre, and the University of Western Australia.
Artificial intelligence is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with higher risk of a future breast cancer diagnosis.
Wendy Ingman, Associate Professor and Study Lead Author, University of Adelaide
The light and dark patterns seen on a mammogram, known as mammographic breast density, have long been recognized as a risk factor for breast cancer.
Now, AI is uncovering additional features within these patterns that could help identify women at the highest risk of developing breast cancer in the future.
“AI methods are now uncovering mammographic features that are stronger predictors of breast cancer risk than any other known risk factor,” added Associate Professor Ingman.
Professor Rik Thompson, Professor of Breast Cancer Research and Domain Leader at QUT's Centre for Genomics and Personalised Health and School of Biomedical Sciences, was the study's senior author.
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. Certain mammographic features could be areas of high oncogenic activity that increases the chance of cancer developing.
Rik Thompson, Study Senior Author and Professor, Queensland University of Technology
Professor Rik Thompson further added, “Critically, we need to identify the pathobiology associated with mammographic features and the underlying mechanisms that link them with breast cancer oncogenesis. It is this common goal that brings us together.”
Associate Professor Helen Frazer, a breast radiologist and leader of research into AI-generated risk scores within the BreastScreen Victoria program, highlighted how this work could open up new possibilities for improving breast cancer screening by tailoring it to better meet 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 personalizing screening to best suit the individual and improve outcomes.
Helen Frazer, Associate Professor, University of Adelaide
Gerda Evans, a breast cancer survivor and Co-Chair of the Australian Breast Density Consumer Advisory Council, has been collaborating with researchers to explore how AI can improve the accuracy of mammography-based risk predictions.
“This is a great advance in predicting breast cancer risk, with potentially huge benefits for the community,” stated Gerda Evans.
Associate Professor Ingman said mammographic density is still a valuable measure of risk at the time of a mammogram.
Ingman added, “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. More information about mammographic breast density can be found on the InforMD website that our research team developed to help de-mystify this breast cancer risk factor.”
Tragically, Professor John Hopper from the University of Melbourne, one of the scientists involved in this research, passed away before its publication. He was deeply committed to the potential of AI-generated mammographic features to transform the future of breast cancer screening.
“With this work, we intend to continue John’s legacy,” concluded Professor Thompson.
Journal Reference:
Ingman, W. V., et al. (2024) Artificial intelligence improves mammography-based breast cancer risk prediction. Patterns. doi.org/10.1016/j.trecan.2024.10.007