论文标题
深度学习预测肺癌筛查的心血管疾病风险低剂量计算机断层扫描
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
论文作者
论文摘要
癌症患者患心血管疾病（CVD）死亡率的风险高于一般人群。用于肺癌筛查的低剂量计算机断层扫描（LDCT）为处于危险患者的同时CVD风险估计提供了机会。我们深入学习的CVD风险预测模型，在国家肺癌筛查试验中接受了30,286个LDCT训练，在曲线（AUC）下达到0.871的区域为0.871，在2085名受试者的单独测试集中，并确定了具有高CVD死亡率的患者（AUC为0.768）。我们针对基于ECG的心脏CT标记验证了我们的模型，包括冠状动脉钙化（CAC）得分，CAD-RADS评分和MESA 10年风险分数，来自335名受试者的独立数据集。我们的工作表明，在高危患者中，深度学习可以将LDCT转化为肺癌筛查的LDCT，以进行双筛查定量工具，以进行CVD风险估计。
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.
