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

基本信息

批准号：
10216066
负责人：
Eric B. Durbin
金额：
$ 64.33万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
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/10216066
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

项目摘要

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.

该项目的目的是丰富具有高质量人群的SEER注册表数据数字病理学，基于机器学习的分类和定量病原体特征集。我们将创建一个高质量的精心策划的存储库数字化病理图像，用于由注册表收集数据的受试者。这些图像将被处理以提取计算特征和建立与注册表数据，因此可以创建信息丰富的人群人群客观成像和临床属性。数字病理学特征的具体例子集合包括定量肿瘤浸润淋巴细胞和分割以及癌症或基质核的表征。功能还将包括光谱和空间潜在病理的签名。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（致病特征）是病理报告中描述的定量替代物。这重要的区别是这些特征是定量和可重现的，与人类不同高度定性并受到高度观察的观察结果可变性。该数据集将提供一种独特的，遍及整个组织的癌症的观点，并动态加速我们对疾病进展阶段，癌症的理解结果，预测和评估治疗效果。这项工作将与三个先知登记处合作进行。我们将合作在项目开发阶段（UG3）期间，新泽西州癌症注册处与新泽西州癌症注册中心。在项目的验证阶段（UH3），佐治亚州和肯塔基州癌症登记处将加入该项目。基础设施将与 SEER注册表确保与注册流程，可伸缩性和能力支持一致创建跨越多个注册表的人群人群。我们将部署视觉分析工具为了促进流行病学研究的人口同伙的创建，请支持在提供高级的同时，可视化特征簇和相关的全曲线图像用于进行基于内容的图像检索的算法。科学验证提出的环境将通过三个有关前列腺癌，淋巴瘤的研究进行。 NSCLC，由三个地点的调查人员领导。