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：
10405657
负责人：
Gari David Clifford
金额：
$ 60.3万
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 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10405657
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 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 machine learning classification neoplasm registry pathology imaging patient population patient stratification population based precision medicine prognostic prototype repository scale up stem therapeutic effectiveness tool treatment response 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.

该项目的目标是通过高质量的基于人群的数据来丰富 SEER 注册数据。数字病理学形式的生物样本数据、基于机器学习的分类和我们将创建一个精心策划的高质量存储库。登记处收集数据的受试者的数字化病理图像。图像将被处理以提取计算特征并与登记数据，从而能够创建信息丰富的人口队列，其中包含客观成像和临床属性的数字病理学衍生特征的具体示例。组包括肿瘤浸润淋巴细胞的量化和分割癌症或基质核的特征还包括光谱和空间特征。这种方法的科学前提源于潜在病理学的特征。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）是病理报告中描述内容的定量替代。重要的区别是这些特征是定量的和可重复的，与人类不同高度定性的观察结果，并受到观察者之间和观察者内部的高度重视该数据集将提供一种独特的、基于人群的组织的癌症视图，并极大地加速我们对疾病进展阶段、癌症的理解结果，并预测和评估治疗效果。这项工作将与三个 SEER 注册机构合作开展。在项目开发阶段 (UG3) 与新泽西州癌症登记处合作。在该项目 (UH3) 的验证阶段，佐治亚州和肯塔基州癌症中心注册管理机构将加入该项目，并与该项目密切合作开发基础设施。 SEER 注册机构确保注册流程、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群体。我们将部署视觉分析工具。促进流行病学研究人群队列的创建，支持工具特征簇和相关全幻灯片图像的可视化，同时提供先进的进行基于内容的图像检索的算法。拟议的环境将通过前列腺癌、淋巴瘤的三项研究进行和非小细胞肺癌，由三个地点的研究人员领导。