Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA
Zengfa Huang,
Yang Yang,
Zheng Wang,
Yunting Hu,
Beibei Cao,
Mei Li,
Xinyu Du,
Xi Wang,
Zuoqin Li,
Wanpeng Wang,
Yi Ding,
Jianwei Xiao,
Yun Hu,
Xiang Wang
Affiliations
Zengfa Huang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Yang Yang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Zheng Wang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Yunting Hu
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Beibei Cao
Department of Community Health, Hanyang District Center For Disease Control and Prevention, Wuhan, Hubei, 430050, China
Mei Li
Department of Community Health, Hanyang District Center For Disease Control and Prevention, Wuhan, Hubei, 430050, China
Xinyu Du
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Xi Wang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Zuoqin Li
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Wanpeng Wang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Yi Ding
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Jianwei Xiao
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China
Yun Hu
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China; Corresponding author.
Xiang Wang
Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430014, China; Corresponding author. Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Avenue, Jiangan, Wuhan, Hubei, 430014, China.
Objectives: The aim of the present study was to investigate the prognostic value of the novel coronary artery disease reporting and data system (CAD-RADS) 2.0 compared with CAD-RADS 1.0 in patients with suspectedcoronary artery disease (CAD) evaluated by convolutional neural networks (CNN) based coronary computed tomography angiography (CCTA). Methods: A total of 1796 consecutive inpatients with suspected CAD were evaluated by CCTA for CAD-RADS 1.0 and CAD-RADS 2.0 classifications. Kaplan-Meier and multivariate Cox models were used to estimate major adverse cardiovascular events (MACE) inclusive of all-cause mortality or myocardial infarction (MI). The C-statistic was used to assess the discriminatory ability of the two classifications. Results: In total, 94 (5.2%) MACE occurred over the median follow-up of 45.25 months (interquartile range 43.53–46.63 months). The annualized MACE rate was 0.014 (95% CI: 0.011–0.017). Kaplan-Meier survival curves indicated that the CAD-RADS classification, segment involvement score (SIS) grade, and Computed Tomography Fractional Flow Reserve (CT-FFR) classification were all significantly associated with the increase in the cumulative MACE (all P < 0.001). CAD-RADS classification, SIS grade, and CT-FFR classification were significantly associated with endpoint in univariate and multivariate Cox analysis. CAD-RADS 2.0 showed a further incremental increase in the prognostic value in predicting MACE (c-statistic 0.702, 95% CI: 0.641–0.763, P = 0.047), compared with CAD-RADS 1.0. Conclusions: The novel CAD-RADS 2.0 evaluated by CNN-based CCTA showed higher prognostic value of MACE than CAD-RADS 1.0 in patients with suspected CAD.
