Framework for radiomics standardization with application in pulmonary CT scans

放射组学标准化框架及其在肺部 CT 扫描中的应用

• 批准号: 10670050
• 负责人: Jianan Grace Gang
• 金额: $ 64.79万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10670050
• 关键词: 3D Print; Affect; Anatomy; Area; Asthma; Attention; Back; Biological Models; Biology; Cadaver; Calibration; Categories; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Communities; Computed Tomography Scanners; Computer software; Computer-Assisted Diagnosis; Custom; Data; Data Collection; Data Set; Data Sources; Databases; Dependence; Diagnostic; Disease; Environment; Evaluation; Excision; Glass; Image; Imaging Device; Institution; Interstitial Lung Diseases; Investigation; Lung; Lung diseases; Machine Learning; Malignant neoplasm of lung; Mathematics; Measures; Methodology; Methods; Modeling; Noise; Normal Range; Output; Patient imaging; Patient-Focused Outcomes; Patients; Pattern; Performance; Protocols documentation; Pulmonary Fibrosis; Radiology Specialty; Recovery; Reporting; Research; Resolution; Scanning; Series; Site; Specific qualifier value; Specimen; Standardization; Structure of parenchyma of lung; System; Techniques; Technology; Testing; Texture; Theoretical model; Time; Tuberculosis; Validation; Vendor; Work; X-Ray Computed Tomography; body system; clinical translation; computerized data processing; data curation; data standards; digital; disease classification; experimental study; falls; feature selection; imaging biomarker; imaging modality; imaging properties; imaging study; imaging system; improved; lung imaging; microCT; model building; model development; predictive modeling; prevent; quantitative imaging; radiomics; reconstruction; research and development; simulation; targeted biomarker; tool

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.

项目概要/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，其广度正在不断扩大 随着对大型患者图像数据库的更广泛访问，放射组学模型已被广泛应用。 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特征，并在具有挑战性的诊断中提供附加信息 放射组学的准确性取决于许多因素。 包括特定成像设备/供应商、采集协议、数据处理等在内的链条是不可取的 通常会对放射组学模型的性能产生巨大影响。 需要仔细的数据管理和协议标准化——通常会阻碍成功或高效的建模 在跨机构、供应商等的大量患者数据聚合中，即使仔细注意 根据协议，许多成像设备（例如 X 射线计算机断层扫描 CT）具有患者特定和扫描特定的图像 继续给放射组学计算增加不良变异性的属性在这项工作中，我们提出了一个。 CT 成像系统端到端建模框架 – 将放射组学计算集成为 这种严格的建模将先前的工作扩展到了底层。 在此背景下，提出了控制成像系统性能的数学框架。 不仅提供了一种基于各种系统依赖来预测放射组学值的机制 降低其准确性的因素；但也为估计潜在“真实”的恢复方法提供了信息； 基于基础生物学的放射组学不受特定图像属性（噪声/分辨率）的影响 我们追求这种放射组学计算的新范式将标准化met- 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，一种肺纹理提供大量诊断信息的应用程序） 疾病）通过以下具体目标： 目标 1：开发放射组学的数学框架 标准化，因此 ILD 放射组学的预测“正向”模型和“反向”恢复模型都将 目标 2：应用并验证预测和标准化。 物理系统中的框架使用具有肺部纹理的定制模型，并包括一系列研究 目标 3：调查影响。 将建立临床 CT 数据放射组学建模性能的标准化。 使用拟议的放射组学模型框架对两个区域进行标准化放射组学的表现 和全肺表征的成功完成将为标准建立一个新的范例。 在多站点数据中应用和验证的标准化放射组学计算为更大、更多的数据打开了大门。 多样化的成像数据集以及更有效地恢复微妙成像生物标志物的潜力。