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; 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.

项目摘要 /摘要 放射组学或成像生物标志物是一个积极的研发领域，广度正在增加 宽度更大，可以访问大型患者图像数据库。放射学模型已在宽阔的 诊断，分类任务和疾病评分范围；具有有效的放射学工作流的优势， 减少错误并突出重要功能，并在挑战诊断中提供其他信息 案例。放射线学的准确性取决于许多因素。与成像相关的可变性 包括特定成像设备/供应商，采集协议，数据处理等的链条是不希望的 并且可以对放射线模型的性能产生巨大影响。通常成功的放射线学模型 需要仔细的数据策划和协议的标准化 - 通常可以防止成功或有效的建模 在跨机构，供应商等大量的患者数据聚集中。 协议，许多成像设备，例如X射线计算机摄影CT具有患者和扫描特异性图像 继续为放射线计算增加不明显可变性的属性。在这项工作中，我们建议 CT成像系统端到端建模的框架 - 将放射线计算集成为 显式阶段和成像系统输出。这种严格的建模将以前的努力扩展到不足 站立并控制成像系统的性能。在这种情况下，提出的数学框架 不仅提供了基于各种系统依赖性预测放射线值值的机制 加密降低其准确性；但也告知恢复方法，以估算基本的“真实” 基于基本生物学的放射科学，由特定图像特性（噪声/分辨率）未腐败 病人形象。我们假设这项用于放射学计算的新范式都将标准化MET- RICS并改善定量。我们将测试这些假设，并将标准化方法应用于放射线学 间质性肺疾病（ILD，肺纹理提供有关有关的大量诊断信息的应用 疾病）通过以下特定目的：目标1：为放射素学开发数学框架 标准化，其中ILD放射线学的预测性“正向”模型和“逆”恢复模型将 可以开发，表征和评估。目标2：应用和验证预测和标准化 物理系统中使用带有肺纹理的自定义幻像的框架，包括一系列研究 所有主要供应商的CT板凳和CT扫描仪的特征良好的CT扫描仪。目标3：调查影响 临床CT数据中放射学模型性能的标准化。多站点研究将建立 在两个区域的放射线学模型中使用拟议框架的标准化放射素学的性能 和整个肺特征。这些目标的成功完成将建立一个新的范式来实现 在多站点数据中应用和验证的弧形放射线计算。这为更大，更多的门打开了 各种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。