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.

项目总结