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: 10375582
• 负责人: Abhinav K Jha
• 金额: $ 48.91万
• 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 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10375582
• 关键词: Address; American College of Radiology Imaging Network; Bayesian Method; Biological Markers; Cardiac; Clinic; Clinical; Collaborations; Consumption; Data; Detection; Diagnosis; Diffusion; Disease; Evaluation; Glycolysis; Goals; Gold; 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; base; 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; 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.

项目概要 定量成像，即根据患者图像计算数字/统计特征，正在作为一种新兴的方法出现。 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， 因此正在开发其中包括重建、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，作为次优方法 会产生不可靠的定量值，因此临床价值有限。执行此评估 与患者成像数据一起使用是非常可取的，但对于大多数人来说黄金标准不可靠或不可用 患者研究使得评估不切实际或不可能。为了能够评估患者的成像方法 数据显示，已经开发了几种非黄金标准评估（NGSE）技术，但主要是在上下文中 的检测任务。最近，我们开发了类似的用于定量任务的 NGSE 技术 和其他人。我们已经证明了 NGSE 技术在对细分方法进行排名方面的有效性 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措施 将这个数学概念转化为临床工具。现有的 NGSE 技术做出的假设可能 在多个 QI 应用程序中不成立，需要大量通常不可用的患者图像，并且具有 仅使用计算研究进行了验证。为了解决这些问题，我们建议开发和 全面验证新颖的广义贝叶斯 NGSE 框架。该框架将是一个通用的 贝叶斯方法将准确反映临床情况，并且不需要进行多个患者研究。这 除了现实的数据之外，还将使用新的拟人化物理模型和患者数据来验证框架 和经过验证的模拟研究。对于临床转化，还需要证明其功效 回答重要临床问题的框架。我们选择的临床问题是使用 NGSE 确定最佳分割方法的框架，用于早期计算 PET 的体积特征 预测非小细胞肺癌（NSCLC）患者的治疗反应。回答这个问题将 帮助解决非小细胞肺癌（NSCLC）个性化治疗策略的关键、紧迫和未满足的需求 发病率和死亡率很高。拟议的 NGSE 框架已准备好加速临床 通过使用患者数据进行评估，转化新的和改进的 QI 方法。该框架将 具有多种高影响力的应用，例如确定测量生物标志物的最佳 QI 方法 监测癌症治疗反应，诊断心脏/神经退行性疾病，并进行成像- 基于剂量测定。因此，开发这个 NGSE 框架有可能对基于 QI 的 临床决策。