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深度学习量化钙评分用于低剂量胸部CT肺筛查时的心血管疾病死亡率自动预测

Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT.

作者信息

de Vos Bob D, Lessmann Nikolas, de Jong Pim A, Išgum Ivana

机构信息

Department of Biomedical Engineering and Physics (B.D.d.V., I.I.), Cardiovascular Institute (B.D.d.V., I.I.), and Department of Radiology and Nuclear Medicine (I.I.), Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, the Netherlands; and Image Sciences Institute (B.D.d.V., N.L., I.I.) and Department of Radiology (P.A.d.J., I.I.), University Medical Center Utrecht, Utrecht, the Netherlands.

出版信息

Radiol Cardiothorac Imaging. 2021 Apr 15;3(2):e190219. doi: 10.1148/ryct.2021190219. eCollection 2021 Apr.

Abstract

PURPOSE

To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality.

MATERIALS AND METHODS

This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test.

RESULTS

The prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69; = .049). Best results were obtained when combining all variables (C statistic, 0.76; < .001).

CONCLUSION

Five-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT.© RSNA, 2021.

摘要

目的

探讨肺部筛查低剂量CT扫描时特定部位动脉钙化量对预测心血管疾病(CVD)死亡率的预后价值。

材料与方法

这项回顾性研究纳入了2002年8月至2004年4月期间参加国家肺部筛查试验并接受低剂量CT扫描的5564名参与者,随访至2009年12月。训练了一个深度学习网络来量化六种血管钙化类型:胸主动脉钙化(TAC);主动脉和二尖瓣钙化;以及左主干、左前降支和右冠状动脉的冠状动脉钙化(CAC)。在胸部CT图像中六个均匀分布且空间对齐的层面上确定TAC和CAC。使用最小绝对收缩和选择算子的多变量逻辑回归进行CVD死亡率预测。将这些方法与使用参与者自我报告的特征(如年龄、吸烟史和疾病史)进行半自动基线预测的方法进行比较。使用非参数DeLong检验对预测模型之间的统计学显著性进行检验。

结果

预测模型使用4451名参与者(中位年龄61岁;37.9%为女性)的数据进行训练,然后在1113名参与者(中位年龄61岁;37.9%为女性)的数据上进行测试。使用钙化评分的预测模型的C统计量为0.74(95%CI:0.69,0.79),优于仅使用参与者特征的基线模型(C统计量为0.69;P = 0.049)。当组合所有变量时获得最佳结果(C统计量为0.76;P < 0.001)。

结论

在肺部筛查低剂量CT扫描时,使用自动提取的基于图像的特征预测五年CVD死亡率是可行的。©RSNA,2021。

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Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT.直接自动冠状动脉钙评分在心脏和胸部 CT 中的应用。
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