利用非对比心脏计算机断层扫描左心房心外膜脂肪组织放射学特征预测心房颤动

IF 2.5 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

CJC Open Pub Date : 2025-07-01 DOI:10.1016/j.cjco.2025.03.024

Shayna Cohen-Dor MD , Moshe Rav-Acha MD, PhD , Fauzi Shaheen MD , Boris Chutko MD , Hadas Labrisch-Kaye MD , Zohar Ben-Haim MD , Yoav Michowitz MD , Hilla Gérard MD , Naama Bogot MD , Shemi Carraso MD , Itzhak Vitkon-Barkay MD , Laurian Copel MD , Michael Glikson MD , Arik Wolak MD

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引用次数: 0

摘要

背景：房颤（AF）的早期检测可以预防房颤相关并发症。心外膜脂肪组织（EAT）放射组学分析显示可预测房颤消融后复发，但关于左房EAT （LA-EAT）放射组学分析预测未知房颤患者房颤的数据有限。我们的目的是基于基于机器学习的LA-EAT放射组学分析和房颤的关联，建立房颤的预测模型。接受非对比心电图门控心脏计算机断层扫描（CT）。使用syngo进行LA-EAT的分割和LA-EAT放射学特征的提取。通过Frontier （Siemens Healthineers, Forchheim, Germany）。单变量分析确定了与AF相关的放射学特征。通过逻辑回归和基于机器学习的随机森林分析建立了AF的预测模型。模型在AF：对照比为1:1的外部队列患者中进行验证，并在AF：对照比为15:85的现实环境中进行部署。结果该研究包括280例患者，其中120例为房颤，160例为对照。基于LA-EAT放射学特征，构建logistic回归和随机森林模型，并在单独的患者内部队列中进行检验，曲线下面积（AUC）分别为0.88和0.86，外部验证验证了这些结果（AUC分别为0.84和0.78）。这两种模型在现实环境队列中得到进一步验证（AUC分别为0.85和0.81）。结论基于非对比心电图门控心脏CT提取的LA-EAT放射学特征的模型可以准确预测房颤，提示一种潜在的无创预测房颤存在的方法。

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Prediction of Atrial Fibrillation Using Radiomic Features of Left Atrial Epicardial Adipose Tissue on Noncontrast Cardiac Computed Tomography

Background

Early detection of atrial fibrillation (AF) can prevent AF-related complications. Radiomic analysis of epicardial adipose tissue (EAT) was shown to predict AF recurrence postablation, but only limited data exist regarding left atrial EAT (LA-EAT) radiomic analysis for predicting AF in patients with yet unknown AF. Our aim was to develop prediction model for AF, based on the association of machine learning-based radiomic analysis of LA-EAT and AF.

Methods

Retrospective matched case-control study of patients with and without AF, undergoing noncontrast electrocardiographic (ECG)-gated cardiac computed tomography (CT). Segmentation of LA-EAT and extraction of LA-EAT radiomic features were performed using syngo.via Frontier (Siemens Healthineers, Forchheim, Germany). Univariate analysis identified radiomic features associated with AF. Predictive models for AF were developed via logistic regression and machine learning-based random forest analyses. Models were validated on external cohort of patients with 1:1 AF : control ratio and deployed in a real-world setting with an AF : control ratio of 15:85.

Results

The study included 280 patients, 120 with documented AF and 160 matched controls. Based on LA-EAT radiomic features, which were significantly associated with AF, logistic regression and random forest models were constructed and tested on separate internal cohort of patients, yielding area under the curve (AUC) of 0.88 and 0.86, respectively, for prediction of AF. External validation verified these results (AUC 0.84 and 0.78, respectively). Both models were further validated in a real-world setting cohort (AUC 0.85 and 0.81, respectively).

Conclusions

Models, based on LA-EAT radiomic features extracted from noncontrast ECG-gated cardiac CT, could accurately predict AF, suggesting a potential widespread noninvasive method for predicting the presence of AF.