Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.

Journal: Nature communications
Published Date: May 20, 2021

Abstract

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.

Authors

  • Hanqing Chao
    Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Hongming Shan
  • Fatemeh Homayounieh
  • Ramandeep Singh
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Ruhani Doda Khera
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Hengtao Guo
  • Timothy Su
    Niskayuna High School, Niskayuna, NY, USA.
  • Ge Wang
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Mannudeep K Kalra
  • Pingkun Yan
    Philips Research North America, Briarcliff Manor, NY 10510, USA.

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