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Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography

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Document pages: 29 pages

Abstract: Cancer patients have a higher risk of cardiovascular disease (CVD) mortalitythan the general population. Low dose computed tomography (LDCT) for lungcancer screening offers an opportunity for simultaneous CVD risk estimation inat-risk patients. Our deep learning CVD risk prediction model, trained with30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an areaunder the curve (AUC) of 0.871 on a separate test set of 2,085 subjects andidentified patients with high CVD mortality risks (AUC of 0.768). We validatedour model against ECG-gated cardiac CT based markers, including coronary arterycalcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from anindependent dataset of 335 subjects. Our work shows that, in high-riskpatients, deep learning can convert LDCT for lung cancer screening into adual-screening quantitative tool for CVD risk estimation.

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