A new model that reliably predicts a woman’s likelihood of developing and then dying of breast cancer within a decade has been developed by researchers from University of Oxford University on the basis of an extensive analysis of data from 11.6 million women aged 20-90 from 2000 to 2020.
All of these women had no prior history of breast cancer. While vital, current breast cancer screening can lead to overdiagnosis and unnecessary treatments, posing challenges to the NHS, the new model could inform a personalised screening approach, targeting women at the highest risk, potentially reducing breast cancer deaths, and reducing unnecessary screenings for lower-risk individuals. Researchers said that the new model works to predict a woman’s 10-year combined risk of developing and then dying from breast cancer.
Identifying women at the highest risk of deadly cancers could improve screening. These women could be invited to start screening earlier, be invited for more frequent screenings, or be screened with different types of imaging.
Such a personalised approach could further lower breast cancer deaths while avoiding unnecessary screening for lower-risk women. Women at higher risk for developing a deadly cancer could also be considered for treatments that try to prevent breast cancers developing. The study is published in the Lancet Digital Health.
Professor Julia Hippisley-Cox, Professor of General Practice and Epidemiology and senior author from the Nuffield Department of Primary Care Health Sciences at the University of Oxford, said: ‘This is an important new study which potentially offers a new approach to screening.
Risk-based strategies could offer a better balance of benefits and harms in breast cancer screening, enabling more personalised information for women to help improve decision making.
Risk based approaches can also help make more efficient use of health service resources by targeting interventions to those most likely to benefit. We thank the many thousands of GPs who have contributed anonymised data to the QResearch database without which this research would not have been possible.’ The researchers tested four different modelling techniques to predict breast cancer mortality risk. Two were more traditional statistical-based models and two used machine learning, a form of artificial intelligence.
All models included the same types of data, like a woman’s age, weight, history of smoking, family history of breast cancer, and use of hormone therapy (HRT). The results showed that one statistical model, developed using ‘competing risks regression’ performed the best overall.
It most accurately predicted which women will develop and die from breast cancer within 10 years. The machine learning models were less accurate, especially for different ethnic groups of women.
Dr Ashley Kieran Clift, first author and Clinical Research Fellow at the Nuffield Department of Primary Care Health Sciences, University of Oxford, said: ‘Funded by Cancer Research UK and taking advantage of the size and richness of the QResearch database with its linked data sources at the University of Oxford, we were able to explore different approaches to develop a tool that might be helpful for new, risk-based public health strategies.