Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the co
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ORIGINAL PAPER
Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score Wenchao Hu1,2 · Xiangjun Wu3,4 · Di Dong3,4 · Long‑Biao Cui1,7 · Min Jiang1,2 · Jibin Zhang1,2 · Yabin Wang1,8 · Xinjiang Wang1 · Lei Gao1 · Jie Tian4,5,6 · Feng Cao1 Received: 14 January 2020 / Accepted: 21 May 2020 © Springer Nature B.V. 2020
Abstract To explore the superiority of radiomics analysis in the diagnostic performance of coronary computed tomography angiography (CCTA) for identifying myocardial ischaemia and predicting major adverse cardiovascular events (MACE). A total of 105 lesions from 88 patients who underwent CCTA and invasive fractional flow reserve measurement were collected as the training set, and another 31 patients with CCTA and clinical outcome information were used as the validation set. Conventional CCTA features included the stenosis diameter, length, Agatston score and high-risk plaque characteristics. After extracting and selecting radiomics features, the robustness of the radiomics features was examined, and then conventional and radiomics models were established using logistic regressions. The area under the receiver operating characteristic (ROC) curve (AUC) and Net Reclassification Index (NRI) were analysed to compare the discrimination and classification abilities between the two models in both the training and validation sets. A total of 1409 radiomics features were extracted, and three wavelet features were finally screened out. The robustness test showed good stability for the refined radiomics features. Compared with the conventional model, the radiomics model displayed a significantly improved diagnostic performance in the training set (AUC 0.762 vs. 0.631, 95% confidence interval [CI] 0.671–0.853 vs. 0.519–0.742, P = 0.058) but a slightly improved diagnostic performance in the validation set (AUC 0.671 vs. 0.592, 95% CI 0.466–0.875 vs. 0.519–0.742, P = 0.448). The NRI of the radiomics model was increased in both the training and validation sets (NRI 0.198 and 0.238, respectively). Quantitative radiomics analysis was feasible and might help to improve the diagnostic performance of CCTA but is still controversial for predicting MACE. Keywords Coronary artery disease · CT angiography · Radiomics · Myocardial ischaemia Abbreviations AUC Area under the curve CCTA Coronary computed tomography angiography FFR Fractional flow reserve LASSO Least absolute shrinkage and selection operator Wenchao Hu and Xiangjun Wu have contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10554-020-01896-4) contains supplementary material, which is available to authorized users. * Jie Tian [email protected] * Feng Cao [email protected] Extended author information available on the last page of the article
LR+ Positive likelihood ratio LR− Negative likelihood ratio MACE Major adverse cardiovascular events NPV Negative predictive value NRI
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