Predicting cardiovascular events from routine mammograms using machine learning.
Authors
Affiliations (12)
Affiliations (12)
- Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia [email protected].
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
- Discipline of Clinical Imaging, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
- Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia.
- University of New South Wales, Sydney, New South Wales, Australia.
- Queensland Digital Health Centre, University of Queensland, Brisbane, Queensland, Australia.
- Professorial Unit, The George Institute for Global Health, Sydney, New South Wales, Australia.
- School of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory, Australia.
- Faculty of Medicine and Health Sciences, The University of Auckland, Auckland, New Zealand.
- The George Institute for Global Health, Sydney, New South Wales, Australia.
- The George Institute for Global Health, School of Public Health, Imperial College London, London, UK.
- School of Public Health, Imperial College London, London, UK.
Abstract
Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises. Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk. We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images. Lifepool is a cohort of women with at least one screening mammogram linked to hospitalisation and death databases. A deep learning model based on DeepSurv architecture was developed to predict major cardiovascular events from mammography images. Model performance was compared against standard risk prediction models using the concordance index, comparative to the Harrells C-statistic. There were 49 196 women included, with a median follow-up of 8.8 years (IQR 7.7-10.6), among whom 3392 experienced a first major cardiovascular event. The DeepSurv model using mammography features and participant age had a concordance index of 0.72 (95% CI 0.71 to 0.73), with similar performance to modern models containing age and clinical variables including the New Zealand 'PREDICT' tool and the American Heart Association 'PREVENT' equations. A deep learning algorithm based on only mammographic features and age predicted cardiovascular risk with performance comparable to traditional cardiovascular risk equations. Risk assessments based on mammography may be a novel opportunity for improving cardiovascular risk screening in women.