Predicting cardiovascular events from routine mammograms using machine learning.

Journal: Heart (British Cardiac Society)
Published Date: Feb 12, 2026

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Authors

  • Jennifer Yvonne Barraclough
    Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia [email protected].
  • Ziba Gandomkar
    Image Optimisation and Perception, Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, NSW, Australia. Electronic address: [email protected].
  • Robert A Fletcher
    Sensyne Health Plc, Schrodinger Building, Heatley Road, Oxford Science Park, Oxford, OX4 4GE, UK.
  • Sebastiano Barbieri
    Centre for Big Data Research in Health, UNSW, Sydney, Australia.
  • Nicholas I-Hsien Kuo
    Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia. [email protected].
  • Anthony Rodgers
    Professorial Unit, The George Institute for Global Health, Sydney, New South Wales, Australia.
  • Kirsty Douglas
    School of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory, Australia.
  • Katrina K Poppe
    Faculty of Medicine and Health Sciences, The University of Auckland, Auckland, New Zealand.
  • Mark Woodward
    The George Institute for Global Health, Sydney, New South Wales, Australia.
  • Blanca Gallego Luxan
    University of New South Wales, Sydney, New South Wales, Australia.
  • Bruce Neal
    The George Institute for Global Health, Sydney, New South Wales, Australia.
  • Louisa Jorm
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
  • Patrick Brennan
    Discipline of Clinical Imaging, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Clare Arnott
    The George Institute for Global Health, University of New South Wales, Sydney, Australia.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals