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.
Highlights
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population
The prediction is calibrated against the incidence of CVD abnormalities during the follow-up period of a clinical trial, subjective assessment of radiologists in reader studies, and the CVD risk scores calculated from electrocardiogram (ECG)-gated cardiac CT including the CAC score[34], CAD-RADS score[35], and MESA 10-year risk score[36]
With a positive predictive value (PPV) of 50.00%, the model achieved a sensitivity of 87.69%, which suggests that our model can identify 87.69% of the CVD-positive subjects using only a chest Low dose computed tomography (LDCT) scan, when allowing half of the positive predictions as false
Summary
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. 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). To tackle the limitations of the prior studies, we propose an end-to-end deep neural network to (a) screen patients for CVDs and (b) quantify CVD mortality risk scores directly from chest LDCT examinations. The prediction is calibrated against the incidence of CVD abnormalities during the follow-up period of a clinical trial, subjective assessment of radiologists in reader studies, and the CVD risk scores calculated from electrocardiogram (ECG)-gated cardiac CT including the CAC score[34], CAD-RADS score[35], and MESA 10-year risk score[36]
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