Methods and Tools for Integrating Pathomics Data into Cancer Registries

将病理组学数据整合到癌症登记处的方法和工具

• 批准号: 10247096
• 负责人: Gari David Clifford
• 金额: $ 62.09万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247096
• 关键词: Advanced Development; Algorithmic Software; Algorithms; Anatomy; Area; Automobile Driving; Biological Assay; Biological Markers; Cell Nucleus; Classification; Clinical; Clinical Data; Cohort Studies; Collaborations; Communities; Cues; Data; Data Set; Development; Diagnostic; Disease; Disease Progression; Ensure; Environment; Evaluation Studies; Exhibits; Eye; Goals; Histopathology; Human; Image; Imaging Device; Informatics; Infrastructure; Intraobserver Variability; Investigation; Kentucky; Link; Lymphoma; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Methodology; Methods; Modernization; Morphology; New Jersey; Non-Small-Cell Lung Carcinoma; Nuclear; Outcome; Pathology; Pathology Report; Patients; Perception; Phase; Phenotype; Population; Process; Registries; Reproducibility; Research; Research Personnel; Resolution; Retrieval; Science; Scientific Evaluation; Site; Slide; Specimen; Testing; Texture; Tissues; Transcend; Tumor Subtype; Tumor-Infiltrating Lymphocytes; Universities; Validation; Visual; Visualization; Work; analytical tool; base; cohort; computer infrastructure; computing resources; data management; data registry; digital pathology; epidemiology study; feature extraction; image archival system; image visualization; improved; informatics infrastructure; interest; neoplasm registry; pathology imaging; patient population; patient stratification; population based; precision medicine; prognostic; prototype; repository; response; scale up; stem; therapeutic effectiveness; tool; tumor; tumor registry; validation studies; whole slide imaging

The goal of this project is to enrich SEER registry data with high‐quality population‐based biospecimen data in the form of digital pathology, machine learning based classifications and quantitative pathomics feature sets. We will create a well‐curated repository of high‐quality digitized pathology images for subjects whose data is being collected by the registries. These images will be processed to extract computational features and establish deep linkages with registry data, thus enabling the creation of information‐rich, population cohorts containing objective imaging and clinical attributes. Specific examples of digital Pathology derived feature sets include quantification of tumor infiltrating lymphocytes and segmentation and characterization of cancer or stromal nuclei. Features will also include spectral and spatial signatures of the underlying pathology. The scientific premise for this approach stems from increasing evidence that information extracted from digitized pathology images (pathomic features) are a quantitative surrogate of what is described in a pathology report. The important distinction being that these features are quantitative and reproducible, unlike human observations that are highly qualitative and subject to a high degree of inter‐ and intra‐observer variability. This dataset will provide, a unique, population‐wide tissue based view of cancer, and dramatically accelerate our understanding of the stages of disease progression, cancer outcomes, and predict and assess therapeutic effectiveness. This work will be carried out in collaboration with three SEER registries. We will partner with The New Jersey State Cancer Registry during the development phase of the project (UG3). During the validation phase of the project (UH3), the Georgia and Kentucky State Cancer Registries will join the project. The infrastructure will be developed in close collaboration with SEER registries to ensure consistency with registry processes, scalability and ability support creation of population cohorts that span multiple registries. We will deploy visual analytic tools to facilitate the creation of population cohorts for epidemiological studies, tools to support visualization of feature clusters and related whole‐slide images while providing advanced algorithms for conducting content based image retrieval. The scientific validation of the proposed environment will be undertaken through three studies in Prostate Cancer, Lymphoma and NSCLC, led by investigators at the three sites.

该项目的目标是用高质量的基于人口的数据来丰富SEER登记册数据 以数字病理学、基于机器学习的分类和 定量病理组学特征集。我们将创建一个精心策划的高质量信息库 登记机构正在收集数据的受试者的数字化病理图像。这些 图像将被处理以提取计算特征并与 登记处数据，从而能够创建信息丰富的人口队列，其中包括 客观的影像和临床属性。数字病理学派生特征的具体示例 SET包括肿瘤浸润性淋巴细胞的定量和分割以及 癌症或间质核的特征。要素还将包括光谱和空间要素 潜在病理的签名。这种方法的科学前提源于 越来越多的证据表明，从数字化病理图像中提取的信息 (病理特征)是病理报告中所描述的定量替代。这个 重要的区别在于，与人类不同，这些特征是定量的和可重复性的 高度定性的观察，并受观察者之间和观察者内部高度关注 可变性。这个数据集将提供一个独特的、基于人群组织的癌症视图， 并极大地加速了我们对疾病进展阶段、癌症阶段的理解 结果，并预测和评估治疗效果。 这项工作将与SEER的三个登记册合作进行。我们将成为合作伙伴 在该项目的开发阶段与新泽西州癌症登记处合作(UG3)。 在项目的验证阶段(UH3)，佐治亚州和肯塔基州癌症 注册处将加入该项目。基础设施将与以下公司密切合作开发 SEER注册表，以确保与注册表流程、可扩展性和能力支持的一致性 创建跨越多个登记处的人口队列。我们将部署可视化分析工具 为促进建立流行病学研究的人群队列，支持 可视化功能簇和相关的全幻灯片图像，同时提供高级 进行基于内容的图像检索的算法。科学地验证了 建议的环境将通过前列腺癌、淋巴瘤的三项研究进行 以及NSCLC，由这三个地点的调查人员领导。