RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATION

提高心房颤动导管消融靶向的放射学方法

• 批准号: 10316365
• 负责人: John Barnard
• 金额: $ 74.9万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10316365
• 关键词: 4q25; Ablation; Affect; Anatomy; Artificial Intelligence; Atlases; Atrial Fibrillation; Biological; Cardiac; Cardiac ablation; Chromosomes; Clinic; Clinical; Clinical Data; Computers; Computing Methodologies; Data; Diagnostic radiologic examination; Disease; Fractals; Genes; Genetic; Genetic Risk; Genomics; Goals; Image; Image Analysis; Imaging Device; Individual; Lead; Left; Left atrial structure; Length; Measurement; Medical; Methods; Modeling; Molecular; Morphology; Myocardial; National Heart, Lung, and Blood Institute; Nomograms; Patient Selection; Patients; Performance; Predisposition; Procedures; Pulmonary veins; Recommendation; Recurrence; Reporting; Research; Risk; Scanning; Site; Slice; Structure; Supervision; Testing; Thick; Variant; X-Ray Computed Tomography; anatomic imaging; auricular appendage; base; cohort; genetic variant; genomic locus; imaging biomarker; imaging platform; improved; insight; machine learning method; non-invasive imaging; novel; predictive marker; prevent; prognostic value; radiomics; reconstruction; risk variant; success; tool; unsupervised learning; voltage

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.

项目摘要 尽管隔离肺静脉（PV）触发器的消融已经彻底改变了房颤（AF）管理，但 进行有效的AF消融仍然具有挑战性。该程序通过定义不明的目标仍有限制 底物，重大并发症的风险为2-6％（单个程序5年成功率低为 17-56％；上次消融后的63-81％）。 NHLBI最近赞助的有关该报告的主要建议 AF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AF消融 成功。明显未满足的非侵入性成像工具需要帮助改善患者选择， 解剖学或医疗疗法的解剖学靶向和个性化。我们的团队发展了小说 分析心脏计算层析成像（CT）扫描的计算成像（放射学）方法 显示出可以预测自动化后反复发生AF的风险（AUC = 0.84，n = 167）。这些方法包括小说 形态学，分形和基于Atlas的特征，嘲笑PV和左心房之间的差异 附录（LAA），仅来自CT扫描的分析。我们建议使用我们的初步数据建立 放射线（计算机提取）功能从放射线图像使用监督和无监督的机器 可以分析左心房（LA）的数字化X线照相和电动动物图像的学习方法 来自两个大型AF消融中心（范德比尔特克利夫兰诊所）的2000多名患者的PV。我们的项目 将着重于解决以下主要目标：1）识别，评估和验证放射线特征和 图像临床列图在消融后预测复发性房颤； 2）识别和验证区域放射线 预测自动化后AF复发的站点，目的是确定患者的个性化目标 进行自动消融； 3）确定放射线特征的生物学相关性，以了解 使用基因组分析，具有解剖学复发性AF的心律失常机制。我们的 3目的将检验以下假设：1）射线照相成像可以检测到预测AF的解剖特征 消融后的复发； 2）区域放射线特征可以预测可以考虑额外的位点 消融; 3）放射线形态特征与电解原特征和基因组相关 与AF易感性相关的变体。开发的工具将使放射线照相和临床整合 可能导致患者选择，解剖学或医疗个性化的数据，这些数据可能会改善患者的选择 疗法。成功的项目完成将产生一个新颖的基于人工智能的成像平台，可以 可以测试对AF消融的个性化靶向，以及对AF的生物基础的见解。