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

项目摘要 下一代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主要公共卫生问题。每6个人中就有1个 死亡在美国。心肌灌注成像（将血液输送到心肌） 灌注单光子发射断层扫描（MPS）允许医生在心脏病发作前检测疾病 发生，目前用于预测每年数百万患者的风险。 在目前的资助下，我们已经建立了一个独特的多中心合作注册中心， 具有预后（主要不良心血管事件）和诊断性的成像数据集（REFINE SPECT） （侵入性导管插入术）结果。使用该注册表，我们已经证明MPS图像的组合 分析和人工智能（AI）工具实现了上级预测性能， 由经验丰富的读者或当前最先进的定量技术进行评估。在更新中，我们计划 使用现有增强数据集（添加CT和心肌血流）扩展REFINE SPECT 信息），并利用最新的人工智能技术为患者提供个性化的决策支持工具， 心血管风险评估和MPS后血运重建的获益估计。 总体目标是优化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AI方法相结合。对于这项工作，我们建议 以下3个具体目标：（1）扩展和增强我们的REFINE SPECT注册，包括CT和MPS血流 数据，（2）为所有MPS和CT图像分析开发全自动化技术，（3）应用可解释的 深度学习时间-事件AI模型，用于最佳预测MACE并从以下血管重建中获益 所有图像和临床数据。 这项工作将产生一个可立即部署的临床工具，它将最佳地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性 以及医生对所有成像（MPS、CT、血流）和临床数据的心理整合。这样的定量 目前还没有综合的方法，只剩下评估风险和建议的现行做法。 治疗是非常主观的。精确的定量结果将以易于理解的方式呈现给临床医生 术语（例如，%每年的风险，或一种治疗与替代治疗的相对风险）。此外，本发明还 我们使人工智能结论更加切实的方法将提高这项技术的采用率。所有结果都将 完全自动导出，从而消除任何可变性。我们的做法将符合现行的MPS做法， 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 风险评估的精确度和准确度提高，从而优化成像在引导 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.