Quantitative framework to predict CTEPH surgical outcome from imaging

项目摘要 “通过影像学预测CTEPH手术结局的定量框架”提案具有长期性 目的是改善慢性血栓栓塞性肺动脉高压（CTEPH）患者与 他们的最佳疗法目前，由于缺乏量化工具， 可供医生使用的指标，以标准化对成像中所见患者疾病的评价。在这一提议中， 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， 以告知疾病严重程度的方式从成像中量化疾病。在第一个目标中，我们使用双- 能量CT图像，从单个研究中捕获血管阻塞的数量和位置， 灌注不足，以及它们之间的关系。这些指标将经过严格设计， 血管系统的所有水平（近端到远端），以捕获一系列闭塞严重度，并使用位置 基于手术治疗可及性的权重。这些指标的效用在于它们能够告知 术前和术后有创血流动力学。我们提案的第二个目的是利用CT肺部 血管造影片来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外科手术预测CTEPH的血管位置（以及手术可及性） 疾病等级分类神经网络将大大有助于系统预测疾病的位置，因为 它们可以分析图像而不会丢失数据，并且还可以合并临床和成像数据。因为 UCSD进行了最大量的肺血栓动脉内膜切除术手术（一种去除肺动脉内膜的手术）。 CTEPH血管阻塞）在世界上，我们是唯一的机构，有所需的数量的预- 手术图像和金标准（手术证实）评估手术疾病水平分类， 训练和评估神经网络方法。在今后的工作中，这些工具可以结合起来， 对CTEPH患者进行系统、定量评价。有了这些标准化评估的指标，我们 将能够量化有助于CTEPH表型的因素，并确定这些成像中的哪一个 表型对手术最敏感。

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.