RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATION

项目摘要 尽管隔离肺静脉（PV）触发的消融已经彻底改变了房颤（AF）的管理， 进行有效的AF消融仍然具有挑战性。该程序仍然受到目标不明确的限制， 基质，2-6%的严重并发症风险和有限的成功率（单次手术5年成功率低至 17-56%;末次消融后63-81%）。NHLBI最近赞助的一份关于 房颤导管消融的研究需求和优先事项是研究心脏结构如何影响房颤消融 成功对于非侵入性成像工具以帮助改进患者选择存在明显的未满足的需求， 解剖靶向和消融或医学治疗的个性化。我们的团队开发了一种新颖的 计算成像（放射组学）方法来分析心脏计算机断层扫描（CT）扫描， 显示可预测消融术后AF复发的风险（AUC=0.84，N=167）。这些方法包括新的 基于形态学、分形和图谱的特征，梳理出肺静脉和左心房之间的差异 左心耳（LAA），仅来自CT扫描分析。我们建议在初步数据的基础上， 从射线照相图像中提取的放射组学（计算机提取）特征，以使用监督和非监督机器 学习可以分析左心房（LA）的数字化X射线照相和电解剖图像的方法 两个大型AF消融中心（Cleveland Clinic，范德比尔特）的2000多名患者的肺静脉和肺静脉。我们的项目 将重点解决以下主要目标：1）识别，评估和验证放射组学特征， 预测消融术后复发性AF的成像-临床列线图; 2）识别并验证局部放射性 预测消融后AF复发的部位，目的是为患者确定个性化靶点 接受AF消融;以及3）识别放射组学特征的生物学相关性，以了解 使用基因组分析，研究房颤复发解剖易感性的潜在致瘤机制。我们 3个目标将测试以下假设：1）放射成像可以检测预测AF的解剖特征 消融后复发; 2）区域放射组学特征可以预测可以考虑进行额外消融的部位。 消融; 3）放射学形态学特征与电解剖学特征和基因组学特征相关 与房颤易感性相关的变异。开发的工具将能够整合放射学和临床 这些数据可能导致改善患者选择、解剖靶向和消融或医疗个性化 治疗项目的成功完成将产生一个新的基于人工智能的成像平台， 进行房颤消融的个性化靶向测试，以及对房颤生物学基础的深入了解。

PROJECT SUMMARY Although ablation to isolate pulmonary vein (PV) triggers has revolutionized atrial fibrillation (AF) management, performing effective AF ablation remains challenging. The procedure remains limited by targeting of ill-defined substrates, a 2-6% risk of major complications and limited success (single procedure 5-year success as low as 17-56%; 63-81% after the last ablation). A major recommendation of a recent NHLBI-sponsored report on the research needs and priorities for AF catheter ablation was to study how cardiac structure affects AF ablation success. There is a clear unmet need for non-invasive imaging tools to aid in improved patient selection, anatomic targeting and personalization of ablation or medical therapies. Our team has developed novel computational imaging (radiomics) methods to analyze cardiac computed tomography (CT) scans that were shown to predict the risk of recurrent AF post-ablation (AUC=0.84, N=167). These approaches included novel morphologic, fractal and atlas based features that teased out differences between PVs and the left atrial appendage (LAA), solely from analyses of CT scans. We propose to build upon our preliminary data using radiomic (computer extracted) features from radiographic images to use supervised and unsupervised machine learning methods that can analyze digitized radiographic and electro-anatomic images from the left atrium (LA) and PVs in over 2000 patients from two large AF ablation centers (Cleveland Clinic, Vanderbilt). Our project will focus on tackling the following main objectives: 1) Identify, evaluate and validate radiomic features and imaging-clinical nomograms predictive of recurrent AF after ablation; 2) Identify and validate regional radiomic sites predictive of post-ablation AF recurrence with the goal of identifying personalized targets for patients undergoing AF ablation; and 3) Identify biological correlates of radiomic features to understand the arrhythmogenic mechanisms underlying anatomic susceptibility to recurrent AF, using genomic analyses. Our 3 aims will test the following hypotheses: 1) Radiographic imaging can detect anatomic features that predict AF recurrence after ablation; 2) Regional radiomic features can predict sites that can be considered for additional ablation; and 3) Radiomic morphologic features are correlated with electroanatomic features and genomic variants associated with AF susceptibility. Tools developed will enable integration of radiographic and clinical data that may lead to improved patient selection, anatomic targeting and personalization of ablation or medical therapies. Successful project completion will yield a novel artificial intelligence-based imaging platform that can be tested for personalized targeting of AF ablation, as well as insights into the biologic basis of AF.