  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, achieved an area
under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and
identified patients with high CVD mortality risks (AUC of 0.768). We validated
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.
