Framework for radiomics standardization with application in pulmonary CT scans

项目摘要/摘要 放射组学，或成像生物标记物，是一个研究和开发的活跃领域，其广度正在增加 更广泛地访问大型患者图像数据库。放射组学模型已被广泛应用于 一系列诊断、分类任务和疾病评分；具有高效放射学工作流程的优势， 减少错误并突出重要功能，并在疑难诊断中提供更多信息 案子。放射组学的准确性取决于许多因素。与成像相关的可变性 不需要包括特定成像设备/供应商、采集协议、数据处理等的链 可以对放射组学模型的性能产生戏剧性的影响。成功的放射组学模型一般 需要仔细的数据管理和协议标准化--通常会妨碍成功或高效的建模 在跨机构、供应商等的患者数据的大型聚合中，即使仔细关注 许多成像设备，如x射线计算机断层扫描CT，都有特定于患者和扫描的图像 继续给放射组学计算增加不希望的可变性的属性。在这项工作中，我们提出了一个 CT成像系统的端到端建模框架--将放射组学计算作为 明确的阶段和成像系统输出。这种严格的建模将之前的努力扩展到 站立并控制成像系统的性能。在这方面，拟议的数学框架 不仅提供了一种基于各种系统依赖来预测放射组学价值的机制 这降低了它们的准确性；但也通知了恢复方法来估计潜在的“真” 基于基本生物学的放射组学不受特定图像属性(噪声/分辨率)的影响 病人的形象。我们假设，这种新的放射组学计算范例将使MET- RICS和改进量化。我们将检验这些假设，并将标准化方法应用于放射组学 间质性肺疾病(ILD)是一种应用程序，其中肺纹理提供了关于 目标1：建立放射组学的数学框架 标准化，其中ILD放射组学的预测性“正向”模型和“反向”恢复模型都将 被开发、表征和评估。目标2：应用和验证预测和标准化 物理系统中的框架，使用带有肺纹理的自定义体模，并包括一系列研究- 在所有主要供应商的功能齐全的CT长凳和CT扫描仪上进行测试。目标3：调查影响 临床CT数据中放射组学建模性能的标准化。一项多地点研究将建立 在区域放射组学模型中使用建议框架的标准化放射组学的性能 以及全肺的特征。这些目标的成功实现将建立一种新的立场-- 在多点数据中应用和验证的标准化放射组学计算。这为更大、更多的 多样化的成像数据集，以及更有效地恢复精细成像生物标记物的潜力。

PROJECT SUMMARY / ABSTRACT Radiomics, or imaging biomarkers, are an active area of research and development that is increasing in breadth with more widespread access to large, patient image databases. Radiomics models have been applied in a wide range of diagnostics, classification tasks, and disease scoring; with advantages for efficient radiology workflow, reducing errors and highlighting important features, and providing additional information in challenging diagnostic cases. Accuracy of radiomics is dependent on a number of factors. The variability associated with the imaging chain including the particular imaging device/vendor, acquisition protocol, data processing, etc. is undesirable and can have a dramatic effect on a radiomics model’s performance. Successful radiomics models generally require careful data curation and standardization of protocols – often preventing successful or efficient modeling in large aggregations of patient data across institutions, vendors, etc. Moreover, even with careful attention to protocol, many imaging devices, like x-ray computed-tomography CT have patient- and scan-specific image properties that continue to add undesirable variability to a radiomics computation. In this work, we propose a framework for end-to-end modeling of a CT imaging system – integrating radiomics calculations as an explicit stage and imaging system output. This kind of rigorous modeling extends previous efforts to under- stand and control the performance of imaging systems. In this context, the proposed mathematical framework provides not only a mechanism for prediction of radiomics values based on the various system depend- ences that degrade their accuracy; but also informs recovery approaches to estimate the underlying “true” radiomics based on the underlying biology uncorrupted by the particular image properties (noise/resolution) of the patient image. We hypothesize that this new paradigm for radiomics computation will both standardize met- rics and improve quantitation. We will test these hypotheses and apply standardization methods to radiomics for interstitial lung disease (ILD, an application where lung textures provide substantial diagnostic information about the disease) through the following specific aims: Aim 1: Develop a mathematical framework for radiomics standardization, wherein both predictive “forward” models and “inverse” recovery models for ILD radiomics will be developed, characterized, and evaluated. Aim 2: Apply and validate prediction and standardization framework in physical systems using custom phantoms with lung textures and including a series of investiga- tions on well-characterized CT benches and CT scanners from all major vendors. Aim 3: Investigate the impact of standardization on radiomics modeling performance in clinical CT data. A multi-site study will establish the performance of standardized radiomics using the proposed framework in radiomics models for both regional and whole lung characterization. Successful completion of these aims will establish a new paradigm for stand- ardized radiomics computation that is applied and validate in multi-site data. This opens the doors to larger, more diverse imaging datasets and the potential for more efficient recovery of subtle imaging biomarkers.