{
  "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": [
    {
      "affiliations": [
        "Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia",
        "Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Jennifer Yvonne Barraclough"
    },
    {
      "affiliations": [
        "Discipline of Clinical Imaging, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia"
      ],
      "name": "Ziba Gandomkar"
    },
    {
      "affiliations": [
        "Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia"
      ],
      "name": "Robert A Fletcher"
    },
    {
      "affiliations": [
        "University of New South Wales, Sydney, New South Wales, Australia",
        "Queensland Digital Health Centre, University of Queensland, Brisbane, Queensland, Australia"
      ],
      "name": "Sebastiano Barbieri"
    },
    {
      "affiliations": [
        "University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Nicholas I-Hsien Kuo"
    },
    {
      "affiliations": [
        "Professorial Unit, The George Institute for Global Health, Sydney, New South Wales, Australia"
      ],
      "name": "Anthony Rodgers"
    },
    {
      "affiliations": [
        "School of Medicine and Psychology, Australian National University, Canberra, Australian Capital Territory, Australia"
      ],
      "name": "Kirsty Douglas"
    },
    {
      "affiliations": [
        "Faculty of Medicine and Health Sciences, The University of Auckland, Auckland, New Zealand"
      ],
      "name": "Katrina K Poppe"
    },
    {
      "affiliations": [
        "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"
      ],
      "name": "Mark Woodward"
    },
    {
      "affiliations": [
        "University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Blanca Gallego Luxan"
    },
    {
      "affiliations": [
        "The George Institute for Global Health, Sydney, New South Wales, Australia",
        "School of Public Health, Imperial College London, London, UK"
      ],
      "name": "Bruce Neal"
    },
    {
      "affiliations": [
        "University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Louisa Jorm"
    },
    {
      "affiliations": [
        "Discipline of Clinical Imaging, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia"
      ],
      "name": "Patrick Brennan"
    },
    {
      "affiliations": [
        "Cardiovascular Division, The George Institute for Global Health, Sydney, New South Wales, Australia",
        "Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia"
      ],
      "name": "Clare Arnott"
    }
  ],
  "title": "Predicting cardiovascular events from routine mammograms using machine learning",
  "uid": "4b03d364-577a-5aba-8334-544ff1237f23"
}