Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-23235-4
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- National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH) [R56HL145172]
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The study highlights the higher risk of cardiovascular disease mortality in cancer patients, and the potential of low dose CT scans for simultaneous cardiovascular risk assessment. A deep learning model developed in the study demonstrates human-level performance, enabling LDCT to be converted into a dual-screening quantitative tool for CVD risk estimation in high-risk patients.
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. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Here, the authors develop a deep learning model to perform this task, showing human-level performance.
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