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

• URL: http://arxiv.org/abs/2008.06997v2
• Date: Mon, 29 Mar 2021 15:15:03 GMT
• Title: Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
• Authors: Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
• Abstract summary: Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. LDCT for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
• Score: 23.614559487371935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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.

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