Quantitative framework to predict CTEPH surgical outcome from imaging

通过影像学预测 CTEPH 手术结果的定量框架

• 批准号: 10389736
• 负责人: Elizabeth M. Bird
• 金额: $ 3.97万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-23 至 2024-01-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10389736
• 关键词: Agreement; Algorithms; Anatomy; Angiography; Benefits and Risks; Blood Vessels; Classification; Clinical; Clinical Data; Clinical assessments; Data; Data Set; Decision Making; Disease; Distal; Evaluation; Excision; Future; Goals; Gold; Health; Image; Impairment; Institution; Knowledge; Lead; Learning; Location; Lung; Machine Learning; Measures; Microvascular Dysfunction; Modernization; Multicenter Studies; Obstruction; Operative Surgical Procedures; Outcome; Patient Care; Patient Selection; Patient imaging; Patients; Perfusion; Phenotype; Physicians; Postoperative Period; Pulmonary Hypertension; Pulmonary Vascular Resistance; Reproducibility; Research; Risk; Severities; Severity of illness; Standardization; Structure; Structure of parenchyma of lung; Surgeon; Techniques; Testing; Thromboendarterectomy; Training; Vascular resistance; Visual; Weight; Work; X-Ray Computed Tomography; alternative treatment; base; career; chronic thromboembolic pulmonary hypertension; convolutional neural network; design; experience; hemodynamics; high resolution imaging; imaging modality; imaging study; improved; innovation; lung volume; mortality; neural network; optimal treatments; prevent; pulmonary vascular disorder; right ventricular failure; surgery outcome; tool

Project Summary The proposal “Quantitative framework to predict CTEPH surgical outcome from imaging” has a long term objective of improving matching of Chronic Thromboembolic Pulmonary Hypertension (CTEPH) patients to their optimum therapy. Currently, advancement of this goal is limited by the lack of quantitative tools and metrics available to physicians to standardize evaluation of patient disease seen on imaging. In this proposal, we aim to tackle two different aspects of this problem. First, we aim to develop metrics to comprehensively quantify disease from imaging in a manner that informs disease severity. In this first aim, we are using dual- energy CT images to capture, from a single study, both the amount and location of vascular obstruction, perfusion deficit, and their relationship to one another. These metrics will be robustly designed to incorporate all levels of the vasculature (proximal to distal), to capture a range of occlusion severities, and to use location weightings based on surgical treatment accessibility. The utility of the metrics will be in their ability to inform both pre and post operative invasive hemodynamics. Our second aim of the proposal is to utilize CT pulmonary angiograms to predict the surgical accessibility of patient disease. We will train convolutional neural networks to predict the vascular location (and therefore surgical accessibility) of CTEPH using the UCSD surgical disease level classification. Neural networks will greatly aid in systematic prediction of disease location, since they can analyze images without data loss, and can also incorporate both clinical and imaging data. Because UCSD performs the highest volume of pulmonary thromboendarterectomy surgeries (a surgery to remove the CTEPH vascular obstructions) in the world, we are the only institution that has the required number of pre- operative images and gold standard (surgically confirmed) assessed surgical disease level classifications to train and evaluate a neural network approach. In future work, these tools can be combined to rapidly, systematically, and quantitatively evaluate CTEPH patients. With these metrics that standardize evaluation, we will be able to quantify factors that contribute to CTEPH phenotypes and determine which of these imaging phenotypes are most responsive to surgery.

项目概要 “通过影像预测 CTEPH 手术结果的定量框架”提案具有长期性 改善慢性血栓栓塞性肺动脉高压 (CTEPH) 患者与 他们的最佳治疗方法。目前，由于缺乏定量工具和方法，这一目标的推进受到限制。 医生可使用的指标来标准化影像学上所见患者疾病的评估。在这个提案中， 我们的目标是解决这个问题的两个不同方面。首先，我们的目标是制定指标来全面评估 通过成像来量化疾病，从而了解疾病的严重程度。在第一个目标中，我们使用双 能量 CT 图像可从一项研究中捕获血管阻塞的数量和位置， 灌注不足及其相互关系。这些指标将经过精心设计，以纳入 脉管系统的所有级别（近端到远端），捕获一系列闭塞严重程度并使用位置 基于手术治疗可及性的权重。指标的效用在于它们能够提供信息 术前和术后有创血流动力学。我们该提案的第二个目标是利用 CT 肺部 血管造影可预测患者疾病的手术可及性。我们将训练卷积神经网络 使用 UCSD 手术预测 CTEPH 的血管位置（以及手术可及性） 疾病级别分类。神经网络将极大地帮助系统预测疾病位置，因为 它们可以在不丢失数据的情况下分析图像，还可以合并临床和成像数据。因为 加州大学圣地亚哥分校进行了最多数量的肺血栓内膜切除术（一种切除血栓的手术） CTEPH 血管阻塞）在世界上，我们是唯一一家拥有所需数量的预 手术图像和金标准（手术证实）评估手术疾病级别分类 训练和评估神经网络方法。在未来的工作中，这些工具可以快速组合起来， 系统地、定量地评估 CTEPH 患者。通过这些标准化评估的指标，我们 将能够量化导致 CTEPH 表型的因素并确定这些成像中的哪些 表型对手术最敏感。