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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要尽管消融隔离肺静脉 (PV) 触发因素彻底改变了心房颤动 (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不明确的限制基质，主要并发症的风险为 2-6%，成功率有限（单次手术 5 年成功率低至 17-56%；最后一次消融后 63-81%）。最近 NHLBI 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。对非侵入性成像工具的明显需求尚未得到满足，以帮助改善患者选择，消融或药物治疗的解剖定位和个性化。我们的团队开发了新颖的用于分析心脏计算机断层扫描 (CT) 扫描的计算成像（放射组学）方法显示可预测消融后 AF 复发的风险（AUC=0.84，N=167）。这些方法包括新颖的基于形态、分形和图集的特征，梳理出 PV 和左心房之间的差异附件 (LAA)，仅来自 CT 扫描分析。我们建议在我们的初步数据的基础上使用来自射线照相图像的放射组学（计算机提取）特征，以使用监督和无监督机器可以分析左心房 (LA) 的数字化放射线和电解剖图像的学习方法来自两个大型 AF 消融中心（克利夫兰诊所、范德比尔特）的 2000 多名患者进行了 PV 和 PV 治疗。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和预测消融后房颤复发的影像临床列线图； 2) 识别和验证区域放射组学预测消融后房颤复发的位点，旨在为患者确定个性化目标正在进行房颤消融； 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导致房颤复发的解剖易感性的致心律失常机制。我们的 3 个目标将测试以下假设：1) 放射线成像可以检测预测 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考虑进行额外治疗的位点消融； 3）放射形态学特征与电解剖特征和基因组相关与 AF 易感性相关的变异。开发的工具将实现放射学和临床的整合可能导致改善患者选择、解剖定位和消融或医疗个性化的数据疗法。项目的成功完成将产生一个新颖的基于人工智能的成像平台，可以进行 AF 消融的个性化定位测试，以及对 AF 生物学基础的深入了解。