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Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score

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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.

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Abbreviations

AUC:

Area under the curve

CCTA:

Coronary computed tomography angiography

FFR:

Fractional flow reserve

LASSO:

Least absolute shrinkage and selection operator

LR+:

Positive likelihood ratio

LR−:

Negative likelihood ratio

MACE:

Major adverse cardiovascular events

NPV:

Negative predictive value

NRI:

Net Reclassification Index

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (91939303, 81820108019, 81971776, 81771924, 91959130, 81930053, 81227901), the National Key R&D Program of China (2017YFC1308700, 2017YFA0205200, 2017YFC1309100, 2017YFA0700401), the Beijing Natural Science Foundation (L182061), Translational Medicine Project of Chinese PLA General Hospital (2017TM-003), and the Youth Innovation Promotion Association CAS (2017175).

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Hu, W., Wu, X., Dong, D. et al. Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score. Int J Cardiovasc Imaging 36, 2039–2050 (2020). https://doi.org/10.1007/s10554-020-01896-4

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