Virtual Intervention of Intracranial Aneurysms

颅内动脉瘤的虚拟干预

• 批准号: 9026656
• 负责人: HUI MENG
• 金额: $ 33.84万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9026656
• 关键词: Accounting; Adopted; Affect; Aftercare; Algorithms; American; Aneurysm; Area; Blood Vessels; Blood flow; Characteristics; Classification; Clinical; Complex; Computational algorithm; Computer Simulation; Data; Development; Devices; Discipline; Engineering; Failure; Fill-It; Future; Geometry; Goals; Grant; Healed; Health; Image; Implant; In Vitro; Institutes; Intervention; Intracranial Aneurysm; Link; Liquid substance; Location; Logistic Regressions; Measures; Methods; Modality; Modeling; Modification; Nature; Neck; Neurosurgeon; Operative Surgical Procedures; Outcome; Output; Parents; Patient-Focused Outcomes; Patients; Performance; Platinum; Postoperative Period; Procedures; Receiver Operating Characteristics; Recurrence; Research Personnel; Residual state; Retreatment; Risk; Rupture; Statistical Data Interpretation; Statistical Models; Stents; Techniques; Testing; Thrombosis; Thrombus; Time; Translating; Treatment Failure; Treatment outcome; Velocimetries; Virtual Tool; base; cohort; combat; computerized tools; cranium; effective therapy; experience; follow-up; healing; hemodynamics; implantable device; implantation; improved; individual patient; innovation; minimally invasive; novel; outcome prediction; particle; post intervention; power analysis; predictive modeling; prevent; prototype; reconstruction; simulation; success; tool; treatment planning; treatment strategy; virtual

 DESCRIPTION (provided by applicant): Endovascular intervention is the predominant mode of for treating intracranial aneurysms (IAs). As a minimally invasive alternative to open-skull surgery, it obliterates an aneurysm by either filling it with platinum coils to decrease inflow and induce aneurysmal thrombosis, or diverting blood flow away using stent-like flow diverters (FDs) to induce gradual aneurysmal occlusion and parent vessel reconstruction. Despite its immense success, 30% of coiled IAs experience recanalization (recurrence), while 10% of FD-treated IAs fail to occlude. Patients experiencing such negative outcomes are subjected to increased risks for IA rupture and complications from treatment. This grant aims at developing a method to predict treatment outcome a priori. Our central hypothesis is that, with other factors, postprocedural hemodynamics predicts endovascular treatment outcome. This proposal aims to both develop clinically-practical computational tools to simulate endovascular treatment strategies and test the above hypothesis by creating predictive models that utilize hemodynamics from computational fluid dynamics (CFD) simulations on cases treated in silico. In Aim 1, we will develop and test rapid simulation tools for coil and FD implantation. Our methods are based on novel ball-winding (coil deployment) and ball-sweeping (FD deployment) algorithms. These methods improve upon existing ones by mimicking clinical deployment strategies with superior computational efficiency. To test if our modeling techniques recapitulate the effects of actual device deployment, we will compare CFD results from treated IAs in silico against hemodynamics experimentally measured by particle image velocimetry in treated patient- specific IA phantoms. In Aim 2, we will test the hypothesis that postprocedural hemodynamics, with other clinical factors, predicts patient angiographic outcome. To this end we will apply virtual intervention retrospectively to 700 treated IA cases at our institute, model post-treatment hemodynamics using CFD, and develop multivariate statistical models for treatment outcome based on patient data. We will use an innovative two-tiered statistical approach to extract models for treatment outcome prediction: discriminant function analysis to pre-screen a large number of candidate variables, followed by multivariate logistic regression for creation of parsimonious predictive models. In Aim 3, we will independently test the models prospectively on a new cohort of 300 treated IAs to determine if the models can correctly predict treatment outcome at 12 months. Successful completion of this project will establish-for the first time-a computational tool to predict IA treatment outcome a priori, thereby enabling neurosurgeons to assess different treatment strategies prior to device deployment. When implemented in the procedure room, this new ability will allow for optimization of treatment for individual patients and development of new strategies for those cases with higher failure rates. This project brings together experienced investigators from multiple disciplines and provides an unprecedented opportunity to translate engineering and computational advancements into clinical usage.

 描述(申请人提供)：血管内介入是治疗颅内动脉瘤(IAS)的主要方式。作为开颅手术的一种微创替代手术，它通过用铂金弹簧圈填充动脉瘤来减少血流和 诱导动脉瘤样血栓形成，或使用支架样血流转向器(FDs)转移血流，以诱导逐渐的动脉瘤闭塞和载瘤血管重建。尽管它取得了巨大的成功，但30%的盘绕IAS经历了再通(复发)，而10%的FD治疗的IAS未能闭塞。经历这种负面结果的患者会面临更高的IA破裂风险和治疗并发症。这笔赠款旨在开发一种事先预测治疗结果的方法。我们的中心假设是，与其他因素一起，术后血流动力学预测血管内治疗结果。这项建议旨在开发临床实用的计算工具来模拟血管内治疗策略，并通过创建预测模型来验证上述假设，该模型利用计算流体动力学(CFD)对矽肺治疗病例的血流动力学模拟。在目标1中，我们将开发和测试线圈和FD植入的快速模拟工具。我们的方法基于新颖的滚珠缠绕(线圈部署)和滚珠扫描(FD部署)算法。这些方法通过模仿具有卓越计算效率的临床部署策略来改进现有方法。为了测试我们的建模技术是否概括了实际设备部署的效果，我们将比较硅胶中治疗的IAS的CFD结果与治疗患者特定IA模体中通过粒子图像测速仪实验测量的血流动力学。在目标2中，我们将检验这一假说，即术后血流动力学与其他临床因素一起预测患者的血管造影结果。为此，我们将在我们研究所对700例接受治疗的IA患者进行回顾性虚拟干预，使用CFD对治疗后血流动力学进行建模，并基于患者数据开发治疗结果的多变量统计模型。我们将使用一种创新的两层统计方法来提取用于治疗结果预测的模型：判别函数分析预先筛选大量候选变量，然后使用多变量Logistic回归创建简约预测模型。在目标3中，我们将在300名接受治疗的IAS的新队列中前瞻性地独立测试这些模型，以确定这些模型是否能够正确预测12个月的治疗结果。该项目的成功完成将首次建立一种计算工具来预先预测IA治疗结果，从而使神经外科医生能够在设备部署之前评估不同的治疗策略。当在程序室实施时，这一新能力将允许对个别患者的治疗进行优化，并为失败率较高的病例开发新的策略。该项目汇集了来自多个学科的经验丰富的研究人员，并提供了一个前所未有的机会，将工程和计算的进步转化为临床应用。