{"title":"左心房周围心外膜脂肪组织与消融后房颤复发的放射学特征。","authors":"Yifan Hu, Longzhe Gao, Qiangrong Wang, Jin Chen, Shanshan Jiang, Genqing Zhou, Jiayin Zhang","doi":"10.1136/openhrt-2025-003364","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to establish a prediction model that incorporates the radiomic features of epicardial adipose tissue (EAT) to predict atrial fibrillation (AF) recurrence after ablation.</p><p><strong>Methods: </strong>We prospectively enrolled patients with AF who underwent pulmonary CT venography before ablation therapy at two hospitals (470 patients in the internal cohort and 81 in the external cohort) between June 2018 and December 2019. Stepwise regression was used to identify clinically relevant factors, including quantitative EAT and left atrial (LA)-EAT measurements (model 1). The random forest algorithm was used to select the radiomic features of EAT and LA-EAT. A radiomics model predicting AF recurrence within 1 year after ablation was developed using these features (model 2). Subsequently, logistic regression was used to integrate radiomic features with clinical data (model 3).</p><p><strong>Results: </strong>In total, 551 patients were enrolled (median age: 66 years, IQR: 60-72 years; 340 men), with 145 experiencing AF recurrence within 1 year. Model 2, based on LA-EAT radiomic features, demonstrated significantly better performance than model 1 (clinical predictive factors and LA-EAT volume) for predicting AF recurrence (areas under the curve (AUC): 0.737 vs 0.584 in the external validation cohort). Model 3 exhibited the highest performance (AUC=0.790 in the external validation cohort, sensitivity value=0.800). Additionally, the combined model provided the highest net clinical benefit within a threshold probability range of 0.2-0.4.</p><p><strong>Conclusions: </strong>The LA-EAT radiomics model along with LA-EAT volume and clinical risk factors exhibited the highest predictive performance for AF recurrence following ablation therapy.</p>","PeriodicalId":19505,"journal":{"name":"Open Heart","volume":"12 2","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12243630/pdf/","citationCount":"0","resultStr":"{\"title\":\"Radiomic features of peri-left atrial epicardial adipose tissue and atrial fibrillation recurrence after ablation.\",\"authors\":\"Yifan Hu, Longzhe Gao, Qiangrong Wang, Jin Chen, Shanshan Jiang, Genqing Zhou, Jiayin Zhang\",\"doi\":\"10.1136/openhrt-2025-003364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study aimed to establish a prediction model that incorporates the radiomic features of epicardial adipose tissue (EAT) to predict atrial fibrillation (AF) recurrence after ablation.</p><p><strong>Methods: </strong>We prospectively enrolled patients with AF who underwent pulmonary CT venography before ablation therapy at two hospitals (470 patients in the internal cohort and 81 in the external cohort) between June 2018 and December 2019. 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引用次数: 0
摘要
目的:本研究旨在建立一个结合心外膜脂肪组织(EAT)放射学特征的预测模型来预测消融后心房颤动(AF)复发。方法:我们前瞻性地招募了2018年6月至2019年12月期间在两家医院接受消融治疗前行肺CT静脉造影的房颤动患者(内部队列470例,外部队列81例)。采用逐步回归方法确定临床相关因素,包括定量EAT和左心房(LA)-EAT测量(模型1)。采用随机森林算法选择EAT和LA-EAT的放射学特征。利用这些特征建立了预测消融后1年内房颤复发的放射组学模型(模型2)。随后,使用逻辑回归将放射学特征与临床数据整合(模型3)。结果:共纳入551例患者(中位年龄:66岁,IQR: 60-72岁;340名男性),145名在1年内发生房颤复发。基于LA-EAT放射学特征的模型2在预测AF复发方面的表现明显优于模型1(临床预测因素和LA-EAT体积)(曲线下面积(AUC): 0.737 vs 0.584)。模型3表现出最高的性能(外部验证队列的AUC=0.790,灵敏度值=0.800)。此外,在0.2-0.4的阈值概率范围内,联合模型提供了最高的净临床效益。结论:LA-EAT放射组学模型、LA-EAT体积和临床危险因素对房颤消融治疗后复发的预测效果最好。
Radiomic features of peri-left atrial epicardial adipose tissue and atrial fibrillation recurrence after ablation.
Objectives: This study aimed to establish a prediction model that incorporates the radiomic features of epicardial adipose tissue (EAT) to predict atrial fibrillation (AF) recurrence after ablation.
Methods: We prospectively enrolled patients with AF who underwent pulmonary CT venography before ablation therapy at two hospitals (470 patients in the internal cohort and 81 in the external cohort) between June 2018 and December 2019. Stepwise regression was used to identify clinically relevant factors, including quantitative EAT and left atrial (LA)-EAT measurements (model 1). The random forest algorithm was used to select the radiomic features of EAT and LA-EAT. A radiomics model predicting AF recurrence within 1 year after ablation was developed using these features (model 2). Subsequently, logistic regression was used to integrate radiomic features with clinical data (model 3).
Results: In total, 551 patients were enrolled (median age: 66 years, IQR: 60-72 years; 340 men), with 145 experiencing AF recurrence within 1 year. Model 2, based on LA-EAT radiomic features, demonstrated significantly better performance than model 1 (clinical predictive factors and LA-EAT volume) for predicting AF recurrence (areas under the curve (AUC): 0.737 vs 0.584 in the external validation cohort). Model 3 exhibited the highest performance (AUC=0.790 in the external validation cohort, sensitivity value=0.800). Additionally, the combined model provided the highest net clinical benefit within a threshold probability range of 0.2-0.4.
Conclusions: The LA-EAT radiomics model along with LA-EAT volume and clinical risk factors exhibited the highest predictive performance for AF recurrence following ablation therapy.
期刊介绍:
Open Heart is an online-only, open access cardiology journal that aims to be “open” in many ways: open access (free access for all readers), open peer review (unblinded peer review) and open data (data sharing is encouraged). The goal is to ensure maximum transparency and maximum impact on research progress and patient care. The journal is dedicated to publishing high quality, peer reviewed medical research in all disciplines and therapeutic areas of cardiovascular medicine. Research is published across all study phases and designs, from study protocols to phase I trials to meta-analyses, including small or specialist studies. Opinionated discussions on controversial topics are welcomed. Open Heart aims to operate a fast submission and review process with continuous publication online, to ensure timely, up-to-date research is available worldwide. The journal adheres to a rigorous and transparent peer review process, and all articles go through a statistical assessment to ensure robustness of the analyses. Open Heart is an official journal of the British Cardiovascular Society.