A radiomic approach to predict myocardial fibrosis on coronary CT angiography in hypertrophic cardiomyopathy. (15th August 2021)

• Record Type: Journal Article
• Authors: Qin, Le; Chen, Chihua; Gu, Shengjia; Zhou, Mi; Xu, Zhihan; Ge, Yingqian; Yan, Fuhua; Yang, Wenjie
• Is Part Of: International journal of cardiology. Volume 337(2021)
• Page Start: 113
• Page End: 118
• Publication Date: 2021-08-15
• Subjects: Hypertrophic cardiomyopathy -- Myocardial fibrosis -- Coronary computed tomography angiography -- Radiomics -- Biomarker; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2021.04.060
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Late gadolinium enhancement (LGE) derived from cardiac magnetic resonance (CMR) represents myocardial fibrosis (MF) and is associated with prognosis in hypertrophic cardiomyopathy (HCM). However, it cannot be evaluated when CMR is unavailable. Hence, we aimed to investigate the ability of radiomic features derived from coronary computed tomography angiography (CCTA) to detect the presence and extent of MF in HCM, with LGE as references. Methods: 161 patients with HCM who underwent CCTA and CMR were retrospectively enrolled and randomly divided into training (107 patients, 1712 segments) and testing cohorts (54 patients, 864 segments). Segments were obtained according to AHA 17-segment method. Radiomic features were extracted from per-segment and entire myocardium regions, and multiple machine-learning algorithms were used for radiomic signatures (Rad-sig) generation and model building. Four models were established by multivariable logistic regression using Rad-sig (R-model), clinical characteristic (C-model), echocardiography parameters (E-model), and all features integrated (Integ-model) to identify LGE/left ventricular mass ≥ 15%. Results: The model achieved good diagnostic accuracy in both training (area under the curve [AUC]:0.81, 95% confidence interval [CI]: 0.78–0.83) and testing cohort (AUC: 0.78, 95%CI: 0.75–0.81) on a per-segment basis for the presence of MF. The Integ-model owned the highest discriminative ability for patients with LGE/left ventricular mass ≥ 15% in both training and testing cohorts with AUC of 0.94 (95%CI: 0.89–0.98) and 0.92 (95%CI: 0.85–0.99), respectively. Conclusions: Our radiomic models were considered as useful and complementary biomarkers for the evaluation of the presence and extent of MF on CCTA, facilitating clinical decision-making and risk stratification in HCM patients. Highlights: Radiomic features derived from CCTA are related to the presence of MF on the segment level. Radiomic features of CCTA can strongly predict the extent of MF with LGE/left ventricular mass ≥ 15%. Radiomic features of CCTA can potentially be a new biomarker for risk stratification of HCM.