A no-gold-standard framework to objectively evaluate quantitative imaging methods with patient data

利用患者数据客观评估定量成像方法的非金标准框架

• 批准号: 10553677
• 负责人: Abhinav K Jha
• 金额: $ 47.23万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10553677
• 关键词: Acceleration; Address; American College of Radiology Imaging Network; Bayesian Method; Biological Markers; Cardiac; Clinic; Clinical; Collaborations; Consumption; Data; Detection; Diagnosis; Diffusion; Disease; Evaluation; Glycolysis; Goals; Heart Diseases; Image; Image Analysis; Lesion; Malignant Neoplasms; Mathematics; Measurement; Measures; Metabolic; Metadata; Methods; Modeling; Monitor; Morbidity - disease rate; Multicenter Trials; Neurodegenerative Disorders; Non-Small-Cell Lung Carcinoma; Patient imaging; Patients; Performance; Positron-Emission Tomography; Prediction of Response to Therapy; Procedures; Process; Progression-Free Survivals; Research Personnel; Role; Smoking History; Techniques; Time; Translating; Tumor Volume; Validation; biomarker development; cancer therapy; clinical application; clinical decision-making; clinical translation; clinically relevant; clinically significant; computer studies; design; dosimetry; efficacy evaluation; fluorodeoxyglucose positron emission tomography; imaging modality; improved; industry partner; mortality; novel; personalized medicine; predictive marker; prognostic value; quantitative imaging; reconstruction; research clinical testing; research study; simulation; single photon emission computed tomography; tool; translational framework; treatment response; tumor

Project Summary Quantitative imaging, where a numerical/statistical feature is computed from a patient image, is emerging as an important tool for diagnosis and therapy planning. Several new and improved quantitative imaging (QI) methods, which include reconstruction, analysis, and estimation methods are thus being developed. There is an important and timely need to optimize the QI methods on the underlying clinical quantitative task, as sub-optimal methods would yield quantitative values that are unreliable, and thus have limited clinical value. Performing this evaluation with patient imaging data is highly desirable, but the unreliability or unavailability of a gold standard for most patient studies makes evaluation impractical or impossible. To enable evaluation of imaging methods with patient data, several no-gold-standard evaluation (NGSE) techniques have been developed, but mostly in the context of detection tasks. More recently, similar NGSE techniques for quantitative tasks have been developed by us and others. We have demonstrated the efficacy of our NGSE technique in ranking segmentation methods for diffusion MR and reconstruction methods for quantitative SPECT. Our goal in this project is to take steps towards translating this mathematical concept to a clinical tool. Existing NGSE techniques make assumptions that may not hold in several QI applications, require large amounts of patient images that are often unavailable, and have been validated using only computational studies. To address these issues, we propose to develop and comprehensively validate a novel generalized Bayesian NGSE framework. This framework will be a generalized Bayesian approach that will reflect clinical scenarios accurately and not require multiple patient studies. The framework will be validated using new anthropomorphic physical phantom and patient data in addition to realistic and validated simulation studies. For clinical translation, it is also necessary to demonstrate the efficacy of the framework in answering an important clinical question. The clinical question we choose is that of using the NGSE framework to determine the optimal segmentation method to compute volumetric features from PET for early prediction of therapy response in patients with non-small cell lung cancer (NSCLC). Answering this question will help address a critical, urgent and unmet need for strategies to personalize the treatment of NSCLC, a disease with high morbidity and mortality rates. The proposed NGSE framework is well poised to accelerate the clinical translation of new and improved QI methods by enabling their evaluation with patient data. The framework will have multiple high-impact applications such as in determining the optimal QI method for measuring biomarkers to monitor cancer-treatment response, diagnose cardiac/neurodegenerative diseases, and conduct imaging- based dosimetry. Thus, developing this NGSE framework has the potential to significantly impact QI-based clinical decision making.

项目摘要 从患者图像计算数值/统计特征的定量成像正在作为一个 诊断和治疗计划的重要工具。几种新的和改进的定量成像（QI）方法， 因此，开发了包括重建，分析和估计方法。有一个重要的 并及时需要优化基础临床定量任务的Qi方法，作为亚最佳方法 将产生不可靠的定量值，因此临床值有限。执行此评估 使用患者成像数据是非常可取的，但是大多数金标准的不可靠性或不可用 患者研究使评估不切实际或不可能。启用患者的成像方法评估 数据，已经开发了几种无金标准评估（NGSE）技术，但主要在上下文中 检测任务。最近，我们已经开发了类似的定量任务NGSE技术 还有其他。我们已经证明了我们的NGSE技术在对分割方法的排序方法中的功效 定量SPECT的扩散MR和重建方法。我们在这个项目中的目标是采取步骤 将这个数学概念转换为临床工具。现有的NGSE技术做出了可能的假设 在几个QI应用中不持有，需要大量的患者图像，而这些图像通常不可用，并且 仅使用计算研究验证。为了解决这些问题，我们建议开发和 全面验证一种新型的广义贝叶斯NGSE框架。这个框架将是一个广义的 贝叶斯方法将准确反映临床方案，不需要多次患者研究。这 框架还将使用新的拟人化物理幻影和患者数据来验证 并经过验证的模拟研究。对于临床翻译，还必须证明 回答一个重要的临床问题的框架。我们选择的临床问题是使用NGSE 框架以确定最佳分割方法以提早计算PET的体积特征 非小细胞肺癌（NSCLC）患者的治疗反应预测。回答这个问题将会 帮助解决对个性化NSCLC治疗的策略的关键，紧急和未满足的需求 发病率和死亡率高。拟议的NGSE框架已经很好地加速了临床 通过使用患者数据启用评估的新质量方法的翻译和改进的Qi方法。框架将 具有多个高影响应用程序，例如确定测量生物标志物的最佳QI方法 为了监测癌症治疗反应，诊断心脏/神经退行性疾病并进行成像 - 基于剂量法。因此，开发此NGSE框架有可能显着影响基于QI的 临床决策。