Litcius/Paper detail

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression

Qian Chen, Guanghui Xie, Chun Xiang Tang, Yang Liu, Pengpeng Xu, Xiaofei Gao, Mengjie Lu, Yunlei Fu, Yingsong Huo, S. Lilly Zheng, Xinwei Tao, Hui Xu, Xindao Yin, Long Jiang Zhang

2023Circulation Cardiovascular Imaging22 citationsDOI

Abstract

BACKGROUND: Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)–based radiomics signature (RS) of plaques for predicting RPP. METHODS: A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RESULTS: RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P =0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P =0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32–4.46]; P =0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset ( P <0.001). CONCLUSIONS: The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Topics & Concepts

MedicineReceiver operating characteristicRadiomicsConfoundingArea under the curveInternal medicineOdds ratioVulnerable plaqueRadiologyCardiologyPathologyNuclear medicineRadiomics and Machine Learning in Medical ImagingCardiac Imaging and DiagnosticsCardiovascular Disease and Adiposity