Virtual Intervention of Intracranial Aneurysms

颅内动脉瘤的虚拟干预

• 批准号: 9279275
• 负责人: HUI MENG
• 金额: $ 34.19万
• 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/9279275
• 关键词: 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; Future; Geometry; Goals; Grant; 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; Ruptured Aneurysm; 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; prospective; prototype; public health relevance; 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（由申请人提供）：血管内介入是治疗颅内动脉瘤（IA）的主要方式。作为开颅手术的微创替代方案，它通过用铂线圈填充动脉瘤以减少流入和消除动脉瘤。 诱导动脉瘤血栓形成，或使用支架样分流器 (FD) 转移血流，以诱导逐渐动脉瘤闭塞和载瘤血管重建。尽管取得了巨大成功，但 30% 的盘绕 IAs 会出现再通（复发），而 10% 的 FD 治疗 IAs 未能闭塞。经历此类负面结果的患者发生 IA 破裂和治疗并发症的风险增加。这笔赠款旨在开发一种先验预测治疗结果的方法。我们的中心假设是，与其他因素一起，术后血流动力学可以预测血管内治疗的结果。该提案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测试上述假设，该预测模型利用计算机流体动力学 (CFD) 模拟治疗病例的血流动力学。在目标 1 中，我们将开发和测试用于线圈和 FD 植入的快速仿真工具。我们的方法基于新颖的绕球（线圈部署）和扫球（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，我们将在计算机中将经过处理的 IA 模型的 CFD 结果与在经过处理的患者特定 IA 模型中通过粒子图像测速法实验测量的血流动力学进行比较。在目标 2 中，我们将检验术后血流动力学与其他临床因素一起预测患者血管造影结果的假设。为此，我们将回顾性地对我们研究所的 700 例 IA 治疗病例应用虚拟干预，使用 CFD 建立治疗后血流动力学模型，并根据患者数据开发治疗结果的多变量统计模型。我们将使用创新的两层统计方法来提取用于治疗结果预测的模型：判别函数分析来预先筛选大量候选变量，然后通过多元逻辑回归来创建简约的预测模型。在目标 3 中，我们将在 300 名治疗 IAs 的新队列中对模型进行前瞻性独立测试，以确定模型是否能够正确预测 12 个月时的治疗结果。该项目的成功完成将首次建立一种计算工具来先验预测 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。当在手术室中实施时，这种新能力将允许优化个体患者的治疗，并为那些失败率较高的病例制定新策略。该项目汇集了来自多个学科的经验丰富的研究人员，并提供了前所未有的机会将工程和计算进步转化为临床应用。