Framework for radiomics standardization with application in pulmonary CT scans

项目总结/摘要 放射组学或成像生物标志物是一个活跃的研究和开发领域，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， 减少错误并突出显示重要特征，并在挑战性诊断中提供额外信息 例放射组学的准确性取决于许多因素。与成像相关的变异性 包括特定成像设备/供应商、采集协议、数据处理等的链是不期望的 并且可以对放射组学模型的性能产生显著影响。成功的放射组学模型通常 需要仔细的数据管理和协议标准化-通常会妨碍成功或有效的建模 在跨机构、供应商等的患者数据的大集合中。此外，即使仔细注意 协议，许多成像设备，如X射线计算机断层扫描CT，具有患者和扫描特定的图像 这些特性继续向放射组学计算增加不期望的可变性。在这项工作中，我们提出了一个 用于CT成像系统的端到端建模的框架-将放射组学计算集成为 显式级和成像系统输出。这种严格的建模扩展了以前的努力， 并控制成像系统的性能。在此背景下，提出的数学框架 不仅提供了一种基于各种系统依赖性的放射组学值预测机制， 降低其准确性的事件;但也为恢复方法提供信息，以估计潜在的“真实” 放射组学基于未被特定图像特性（噪声/分辨率）破坏的基础生物学， 患者形象。我们假设，这种新的放射组学计算模式将标准化金属， rics和改进定量。我们将测试这些假设，并将标准化方法应用于放射组学， 间质性肺疾病（ILD），肺纹理提供关于以下方面的实质性诊断信息的应用 目标1：建立放射组学的数学框架 标准化，其中用于ILD放射组学的预测“正向”模型和“反向”恢复模型将 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具有肺部纹理的自定义体模，并包括一系列研究 所有主要供应商的CT工作台和CT扫描仪上的特征。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。一项多中心研究将建立 标准化放射组学的性能，使用放射组学模型的区域 和整个肺的特征。这些目标的顺利实现将为我国的林业发展建立一个新的范式， 标准化的放射组学计算，在多站点数据中应用和验证。这为更大、更 多样化的成像数据集和更有效地恢复精细成像生物标志物的潜力。

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.