Yang Min, Cao Qiqi, Xu Zhihan, Ge Yingqian, Li Shujiao, Yan Fuhua, Yang Wenjie
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Siemens Healthineers Computed Tomography (CT) Collaboration, Shanghai, China.
Front Cardiovasc Med. 2022 Mar 3;9:813085. doi: 10.3389/fcvm.2022.813085. eCollection 2022.
This study aimed to evaluate the feasibility of differentiating the atrial fibrillation (AF) subtype and preliminary explore the prognostic value of AF recurrence after ablation using radiomics models based on epicardial adipose tissue around the left atrium (LA-EAT) of cardiac CT images.
The cardiac CT images of 314 patients were collected wherein 251 and 63 cases were randomly enrolled in the training and validation cohorts, respectively. Mutual information and the random forest algorithm were used to screen for the radiomic features and construct the radiomics signature. Radiomics models reflecting the features of LA-EAT were built to differentiate the AF subtype, and the multivariable logistic regression model was adopted to integrate the radiomics signature and volume information. The same methodology and algorithm were applied to the radiomic features to explore the ability for predicting AF recurrence.
The predictive model constructed by integrating the radiomic features and volume information using a radiomics nomogram showed the best ability in differentiating AF subtype in the training [AUC, 0.915; 95% confidence interval (CI), 0.880-0.951] and validation (AUC, 0.853; 95% CI, 0.755-0.951) cohorts. The radiomic features have shown convincible predictive ability of AF recurrence in both training (AUC, 0.808; 95% CI, 0.750-0.866) and validation (AUC, 0.793; 95% CI, 0.654-0.931) cohorts.
The LA-EAT radiomic signatures are a promising tool in the differentiation of AF subtype and prediction of AF recurrence, which may have clinical implications in the early diagnosis of AF subtype and disease management.
本研究旨在评估基于心脏CT图像左心房周围心外膜脂肪组织（LA-EAT）的放射组学模型区分房颤（AF）亚型的可行性，并初步探讨消融术后AF复发的预后价值。
收集314例患者的心脏CT图像，其中251例和63例分别随机纳入训练组和验证组。采用互信息和随机森林算法筛选放射组学特征并构建放射组学特征图谱。建立反映LA-EAT特征的放射组学模型以区分AF亚型，并采用多变量逻辑回归模型整合放射组学特征图谱和容积信息。将相同的方法和算法应用于放射组学特征，以探索预测AF复发的能力。
使用放射组学列线图整合放射组学特征和容积信息构建的预测模型在训练组（AUC，0.915；95%置信区间[CI]，0.880-0.951）和验证组（AUC，0.853；95%CI，0.755-0.951）中区分AF亚型的能力最佳。放射组学特征在训练组（AUC，0.808；95%CI，0.750-0.866）和验证组（AUC，0.793；95%CI，0.654-0.931）中均显示出对AF复发有可信的预测能力。
LA-EAT放射组学特征图谱是区分AF亚型和预测AF复发的一种有前景的工具，可能对AF亚型的早期诊断和疾病管理具有临床意义。
