RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATION

项目总结 尽管通过消融来隔离肺静脉(PV)触发已经彻底改变了房颤(AF)的治疗， 进行有效的房颤消融仍然具有挑战性。该程序仍然受到目标定义不明确的限制 底物，2-6%的主要并发症风险和有限的成功(单一手术5年成功率低至 17-56%；最后一次消融后63-81%)。最近由NHLBI赞助的一份报告提出了一项主要建议 房颤导管消融的研究需要和重点是研究心脏结构如何影响房颤消融 成功。对于帮助改进患者选择的非侵入性成像工具存在明显未得到满足的需求， 消融或医疗治疗的解剖靶向和个性化。我们的团队已经开发出了小说 用于分析心脏CT扫描的计算成像(放射组学)方法 可以预测消融后房颤复发的风险(AUC=0.84，N=167)。这些方法包括新颖的 基于形态学、分形学和图谱的特征梳理出静脉曲张和左房的差异 附件(LAA)，仅根据CT扫描分析。我们建议在初步数据的基础上使用 使用监督和非监督机器从射线图像中提取放射体特征 分析左心房(LA)数字化X线片和电子解剖图像的学习方法 来自两个大型房颤消融中心(克利夫兰诊所，范德比尔特)的2000多名患者。我们的项目 将重点解决以下主要目标：1)识别、评估和验证放射学特征和 影像-临床标准图预测消融后房颤复发；2)识别和验证区域放射学 预测消融后房颤复发的部位，目的是确定患者的个性化靶点 进行房颤消融；以及3)确定放射组学特征的生物学相关性，以了解 利用基因组分析，研究房颤复发解剖易感性的致心律失常机制。我们的 3AIMS将检验以下假设：1)放射成像可以检测到预测房颤的解剖特征 消融后复发；2)区域放射学特征可预测可考虑的额外部位 3)放射学形态特征与电解剖特征和基因组学特征相关 与房颤易感性相关的变异。开发的工具将使放射学和临床相结合 可改进患者选择、解剖定位和个人化消融或医疗的数据 治疗。成功完成项目将产生一种基于人工智能的新型成像平台，该平台可以 对房颤消融的个性化靶向进行测试，以及对房颤的生物学基础的洞察。

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.