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

Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang (), Mannudeep K. Kalra () and Pingkun Yan ()
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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

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23235-4

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DOI: 10.1038/s41467-021-23235-4

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