Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China.
Department of Cardiovascular Surgery, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, No. 197 Ruijin 2nd Rd, Shanghai 200025, China.
Int J Cardiol. 2021 Aug 15;337:113-118. doi: 10.1016/j.ijcard.2021.04.060. Epub 2021 May 5.
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.
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%.
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.
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.
心脏磁共振（CMR）衍生的晚期钆增强（LGE）代表心肌纤维化（MF），与肥厚型心肌病（HCM）的预后相关。然而，当 CMR 不可用时，无法对其进行评估。因此，我们旨在研究从冠状动脉计算机断层扫描血管造影（CCTA）获得的放射组学特征检测 HCM 中 MF 的存在和程度的能力，以 LGE 为参考。
回顾性纳入 161 例接受 CCTA 和 CMR 的 HCM 患者，随机分为训练集（107 例，1712 个节段）和测试集（54 例，864 个节段）。节段按 AHA 17 节段法获得。从每个节段和整个心肌区域提取放射组学特征，并使用多种机器学习算法生成放射组学特征（Rad-sig）并构建模型。使用多变量逻辑回归通过 Rad-sig（R 模型）、临床特征（C 模型）、超声心动图参数（E 模型）和所有特征综合（Integ 模型）建立四个模型，以识别 LGE/左心室质量≥15%。
该模型在训练集（AUC：0.81，95%置信区间 [CI]：0.78-0.83）和测试集（AUC：0.78，95%CI：0.75-0.81）上，基于每个节段的 MF 存在情况，均实现了良好的诊断准确性。在训练和测试队列中，Integ 模型对 LGE/左心室质量≥15%的患者具有最高的鉴别能力，AUC 分别为 0.94（95%CI：0.89-0.98）和 0.92（95%CI：0.85-0.99）。
我们的放射组学模型被认为是 CCTA 评估 MF 存在和程度的有用且互补的生物标志物，有助于 HCM 患者的临床决策和风险分层。
