Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT

项目总结 新一代SPECT和CT对疾病和预后的定量预测 冠状动脉疾病仍然是世界范围内的一个主要公共卫生问题。它导致大约每6个人中就有1人 美国的死亡人数。心肌灌注(将血液输送到心肌)的心肌成像 灌注单光子发射断层扫描(MPS)使医生能够在心脏病发作前发现疾病。 发生，目前每年被用于预测数百万患者的风险。 在目前的赠款下，我们建立了一个独特的协作多中心登记册，其中包括23,000多个 具有预后(主要不良心血管事件)和诊断的成像数据集(改进SPECT) (侵入性导管术)结果。使用这个注册表，我们已经演示了MPS图像的组合 与可视化工具相比，分析和人工智能(AI)工具实现了卓越的预测性能 由有经验的读者或当前最先进的量化技术进行评估。在更新方面，我们计划 使用现有的增强数据集(添加CT和心肌血流量)扩展Refine SPECT 信息)，并利用最新的人工智能技术为特定患者提供个性化决策支持工具 MPS后心血管风险评估和血管重建术的益处评估。 总体目标是通过以下方式优化MPS在风险预测和治疗指导方面的临床能力 将所有可用的成像和临床数据与最先进的人工智能方法集成在一起。对于这项工作，我们建议使用 以下三个具体目标：(1)扩大和加强我们的精炼SPECT注册，包括CT和MPS流程 数据，(2)为所有MPS和CT图像分析开发完全自动化的技术，(3)应用可解释的 深度学习-事件间隔时间AI模型，用于最优预测MACE并受益于 所有影像和临床数据。 这项工作将产生一个可立即部署的临床工具，该工具将以最佳方式预测不良事件的风险 并确定特定疗法的相对益处，这超出了主观视觉分析的可能 以及医生对所有影像(MPS、CT、FLOW)和临床数据的心理集成。这样的数量 还没有综合的方法，留下了目前评估风险和建议的做法 治疗主观性很强。精确的定量结果将以易于理解的方式呈现给临床医生。 特定患者的术语(例如，每年的风险百分比，或一种疗法与另一种疗法的相对风险)。另外， 我们使人工智能结论更加有形的方法将促进这项技术的采用。所有结果都将是 完全自动派生，从而消除了任何变异性。我们的方法将适合目前的MPS实践，并将 可以立即翻译到世界各地的诊所。最重要的是，这项研究将使患者受益 提高风险评估的精确度和准确度，从而优化成像在指导中的使用 患者管理决策，并最终改善结果。

PROJECT SUMMARY Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT Coronary artery disease remains a major public health problem worldwide. It causes approximately 1 of every 6 deaths in the United States. Imaging of myocardial perfusion (delivery of blood to the heart muscle) by myocardial perfusion single photon emission tomography (MPS) allows physicians to detect disease before heart attacks occur and is currently used to predict risk in millions of patients annually. Under the current grant, we have established a unique collaborative multicenter registry including over 23,000 imaging datasets (REFINE SPECT) with both prognostic (major adverse cardiovascular events) and diagnostic (invasive catheterization) outcomes. Using this registry, we have demonstrated that a combination of MPS image analysis and artificial intelligence (AI) tools achieved superior predictive performance compared to visual assessment by experienced readers or current state-of-the-art quantitative techniques. In the renewal, we plan to expand REFINE SPECT with now-available enhanced datasets (adding CT and myocardial blood flow information) and leverage latest AI advances to provide a personalized decision support tool for patient-specific cardiovascular risk assessment and estimation of benefit from revascularization following MPS. The overall aim is to optimize the clinical capabilities of MPS in risk prediction and treatment guidance by integrating all available imaging and clinical data with state-of-the-art AI methods. For this work, we propose the following 3 specific aims: (1) To expand and enhance our REFINE SPECT registry including CT and MPS flow data, (2) To develop fully automated techniques for all MPS and CT image analysis, (3) To apply explainable deep learning time-to-event AI models for optimal prediction of MACE and benefit from revascularization from all image and clinical data. This work will result in an immediately deployable clinical tool, which will optimally predict risk of adverse events and establish the relative benefits from specific therapies, beyond what is possible by subjective visual analysis and mental integration of all imaging (MPS, CT, flow), and clinical data by physicians. Such quantitative integrative methods are not yet available, leaving the current practice for assessing risk and recommending therapy highly subjective. The precise quantitative results will be presented to clinicians in easy to understand terms (e.g., % risk per year, or relative risk of one therapy vs. the alternative) for a specific patient. Additionally, our methods to make AI conclusions more tangible will improve adoption of this technology. All results will be derived fully automatically thus eliminating any variability. Our approach will fit into current MPS practice and will be immediately translatable to clinics worldwide. Most importantly, this research will allow patients to benefit from increased precision and accuracy in risk assessment, thereby optimizing the use of imaging in guiding patient management decisions and ultimately improving outcomes.