MyBRISK’s Professor John Hopper has been working in the BRAIx program, with partners St Vincent’s Institute of Medical Research, BreastScreen Victoria, St Vincent’s Hospital Melbourne, and the Australian Institute of Machine Learning at the University of Adelaide, that is jointly developing AI models to improve breast cancer screening. Professor Hopper and colleagues have recently discovered a new mammogram-based measure of breast cancer risk (BRAIx risk score) that can predict the risk of developing breast cancer better than all known genetic factors.
Now, the National Breast Cancer Foundation has announced a $1.5 million grant to a global team of researchers from MyBRISK, and universities in Australia, Korea, Malaysia and Colombia who will examine novel methods to investigate the genetic and lifestyle factors contributing to the BRAIx risk score.
The project, A Study of Twins and Sisters for Predicting Breast Cancer Risk From Mammograms, will involve digital mammograms, blood samples and personal information being collected from pairs of twins and sisters with and without breast cancer. These findings could potentially lead to ways to lower the risk of breast cancer and identify women and their families at high risk.
Research team
The project’s Chief and Associate Investigators are:
- Professor John Hopper, Head of MyBRISK CRE, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne
- Doctor Sue Malta, Senior Research Fellow and Project Manager of MyBRISK CRE, CEB, MSPGH, UoM
- Dr Lucas Calais Ferriera, Senior Research Fellow, CEB, MSPGH, UoM
- Associate Professor Michelle Reintals, Clinical Director and Head, Radiology, BreastScreen South Australia
- Professor Melissa Southey, Chair of Precision Medicine, School of Clinical Sciences, Monash Health, Monash University
- Professor Enes Makalic, Centre for Epidemiology and Biostatistics, MSPGH, UoM
- Associate Professor Robert MacInnis, Epidemiology Division, Cancer Council Victoria
- Associate Professor Shuai Li, Centre for Epidemiology and Biostatistics, MSPGH, UoM
- Professor Joohon Sung, Department of Epidemiology, Seoul National University, Korea
- Dr Maxine Tan, Monash University Malaysia
- Professor Said Pertuz, Director, Connectivity and Signal Processing Research Group, Industrial University of Santander
- Gerda Evans, consumer representative
- Heather Worland, consumer representative
Why is this research needed?
While breast screening mammograms have been a standard practice in Australia and the effectiveness is evident in the reduction of breast cancer related deaths, screening protocols have remained largely unaltered. Improving breast cancer screening by identifying the women most at risk of breast cancer, automatically at the time of their mammograms, could achieve better survival outcomes and further reduce deaths from this disease.
Expected outcomes
Successful outcomes of this study will reveal new breast cancer risk factors and how a combination of these factors contribute to the mammogram-based measure of breast cancer risk. Implementation into population breast screening could help identify women at substantial risk of the disease not previously recognised.
Project description
Mammograms contain information that can predict breast cancer risk. Prior research by Prof John Hopper at the University at Melbourne in the BRAIx program, with partners St Vincent’s Institute of Medical Research, BreastScreen Victoria, St Vincent’s Hospital Melbourne, and the Australian Institute of Machine Learning at the University of Adelaide, led to a breakthrough in breast cancer risk prediction. The team discovered that specific features on a digital mammogram, when read by AI models, generate a novel risk score (BRAIx Risk Score) that predicts breast cancer risk better than all known genetic factors.
With NBCF support, this new research project will collect digital mammograms, blood samples and personal information (family history, lifestyle) from 1,000 twins and sisters whom they have previously studied, and from a further 1,000 twins without breast cancer, and an additional 100 pairs twins affected by breast cancer. The twin family study design can provide information about both genetic and non-genetic factors. Using state of the art technologies the team will investigate the genetic and lifestyle-related factors that underly BRAIx Risk Score and determine if and how these factors cause breast cancer.
Using innovative approaches this study will produce new findings about what causes breast cancer and how to improve screening and lower breast cancer mortality.