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

Author

Listed:
  • Hanqing Chao

    (Rensselaer Polytechnic Institute)

  • Hongming Shan

    (Rensselaer Polytechnic Institute)

  • Fatemeh Homayounieh

    (Harvard Medical School)

  • Ramandeep Singh

    (Harvard Medical School)

  • Ruhani Doda Khera

    (Harvard Medical School)

  • Hengtao Guo

    (Rensselaer Polytechnic Institute)

  • Timothy Su

    (Niskayuna High School)

  • Ge Wang

    (Rensselaer Polytechnic Institute)

  • Mannudeep K. Kalra

    (Harvard Medical School)

  • Pingkun Yan

    (Rensselaer Polytechnic Institute)

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.

Suggested Citation

  • Hanqing Chao & Hongming Shan & Fatemeh Homayounieh & Ramandeep Singh & Ruhani Doda Khera & Hengtao Guo & Timothy Su & Ge Wang & Mannudeep K. Kalra & Pingkun Yan, 2021. "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23235-4
    DOI: 10.1038/s41467-021-23235-4
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