Classification and Prognostication in Pulmonary Thromboembolism Using Computed Tomography Image Analytics

使用计算机断层扫描图像分析对肺血栓栓塞进行分类和预测

• 批准号: 10502716
• 负责人: FARBOD Nicholas RAHAGHI
• 金额: $ 76.87万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10502716
• 关键词: Acute; Acute Disease; Awareness; Blood Circulation; Blood Vessels; Cardiac; Cardiovascular system; Cause of Death; Cessation of life; Chronic; Chronic Disease; Classification; Clinical; Clinical Data; Coagulation Process; Complex; Coupled; Data; Development; Diagnosis; Diagnostic; Disease; Distal; Dyspnea; Embolism; European; Evaluation; Evolution; Exercise; Functional disorder; Future; Gases; General Hospitals; Heart; Heart failure; Hospitals; Image; Image Analysis; Imaging Device; Impairment; Individual; Intercept; Intervention; Lung; Massachusetts; Methods; Modeling; Morbidity - disease rate; Obstruction; Operative Surgical Procedures; Outcome; Patient Care; Patients; Phenotype; Prospective Studies; Pulmonary Circulation; Pulmonary Embolism; Pulmonary Hypertension; Pulmonary Thromboembolism; Registries; Reporting; Reproducibility; Residual state; Resolution; Right Ventricular Dysfunction; Risk; Scanning; Selection for Treatments; Severities; Severity of illness; Specialized Center; Symptoms; Time; Universities; Vascular Diseases; Western World; Woman; X-Ray Computed Tomography; artery occlusion; base; biomedical referral center; chronic thromboembolic pulmonary hypertension; clinical care; clinical decision-making; deep learning; disease classification; experience; follow-up; functional disability; hemodynamics; image processing; imaging approach; imaging modality; improved; lung pressure; mortality; novel; outcome prediction; predicting response; prognostication; pulmonary arterial hypertension; quantitative imaging; serial imaging; surgery outcome; treatment choice; treatment response

Project Summary Pulmonary thromboembolism remains a significant cause of morbidity and mortality in the western world. While many of the initial symptoms in acute pulmonary embolism (PE) resolves with appropriate treatment, there is increasing awareness of chronic impact of the disease ranging from development of chronic thromboembolic pulmonary hypertension (CTEPH) to persisting dyspnea and exercise impairment. Many patients initially diagnosed with PE may already have chronic disease and inappropriate treatment for acute disease in these cases may be harmful and delay referral to specialized centers with experience in treating chronic disease. On the other hand many patients with acute PE go on to develop chronic disease despite current treatment options and follow-up to insure resolution remains a challenge particularly without the ability to predict who will develop chronic disease. Furthermore, prognostication and selection of treatments can be difficult, particularly in submassive acute PE and CTEPH, particularly with newly emerging treatment choices. Quantitative methods are needed to help define disease trajectories early in presentation, help guide prognostication and treatment and improve our understanding of the pathophysiology of this condition. Computed Tomography (CT) imaging is the cornerstone of evaluation of pulmonary thromboembolism. In acute PE, it is the often the first imaging modality available for assessing treatment options. As the patient recovers, it is used to detect chronic or reoccurring clot guide interventions in chronic disease. Advances in CT imaging quality, image processing (including application of deep learning), coupled with increasing computation power make possible the extraction of a large number of novel features from CT imaging. In this proposal we seek to combine our team’s experience in CT image quantification with multi-center longitudinal data to develop CT imaging features that can identify and predict disease chronicity, its impact on the pulmonary circulation and its response to treatment. In Aim 1 we utilize longitudinal data from three academic hospitals (Brigham and Women’s Hospital, Massachusetts General Hospital, Northwestern University) to assess CT features at presentation that predict the presence or development of chronic disease. In Aim 2, we study both the presentation and follow-up image to build quantitative models of the impact of acute and chronic disease on the pulmonary circulation in order to help with prognostication and improve non-invasive methods of predicting the relevance of persistent disease to the clinical state of patients. In aim 3 we use a combination of longitudinal imaging in CTEPH patients having undergone surgery and patients with pulmonary arterial hypertension to identify patients that would have the most optimal surgical outcomes. We believe that the combination of the features and models developed in these complementary aims will advance our ability to use clinically available CT imaging to improve phenotyping, prognostication and treatment decisions and improve our understanding of the longitudinal progression of pulmonary thromboembolic disease.

项目摘要 肺血栓栓塞仍然是西方世界发病和死亡率的重要原因。尽管 急性肺栓塞（PE）中的许多初始症状通过适当的治疗解决，有 对疾病慢性影响的认识越来越多，范围从慢性血栓形成的发展范围 肺动脉高压（CTEPH）持续呼吸困难和运动障碍。最初许多患者 被诊断出患有PE的可能已经患有慢性疾病和不适当的急性疾病治疗 病例可能是有害的，并且延迟转介给具有治疗慢性疾病经验的专业中心。在 另一方面，许多患有急性PE的患者继续开发慢性疾病目的地当前治疗 选择和确保解决方案的后续行动仍然是一个挑战，特别是没有预测谁将 发展慢性病。此外，促进和选择治疗可能很困难，尤其是 在急性PE和CTEPH中，尤其是新兴的治疗选择。定量 需要方法来帮助定义疾病轨迹的早期，帮助指导提示和 治疗并提高我们对这种疾病的病理生理学的理解。 计算机断层扫描（CT）成像是评估肺血栓栓塞的基石。在 急性PE，这通常是用于评估治疗方案的第一个成像方式。作为病人 恢复后，它用于检测慢性疾病中的慢性或重复凝块指导干预措施。 CT的进步 成像质量，图像处理（包括深度学习的应用），并增加 计算能力使从CT成像中提取了大量新型功能。在这个 提案我们寻求将团队在CT图像量化中的经验与多中心纵向结合 数据以开发CT成像功能，可以识别和预测疾病的慢性，其对 肺圈及其对治疗的反应。在AIM 1中，我们利用三个学术的纵向数据 医院（马萨诸塞州西北大学的马萨诸塞州总医院的杨树和妇女医院） 评估介绍中的CT特征，以预测慢性疾病的存在或发展。在AIM 2中，我们 研究演示和后续图像，以构建急性和慢性影响影响的定量模型 肺循环的疾病，以帮助提示和改善非侵入性方法 预测持续性疾病与患者临床状态的相关性。在AIM 3中，我们使用组合 接受手术和肺动脉患者的CTEPH患者的纵向成像 高血压以识别将具有最佳手术结果的患者。我们相信 这些互补目标中开发的功能和模型的结合将提高我们使用的能力 临床上可用的CT成像以改善表型，编程和治疗决策并改善 我们对肺血栓疾病的纵向进展的理解。