Methods and Tools for Integrating Pathomics Data into Cancer Registries

本项目的目标是使用高质量的基于人群的数据丰富SEER登记研究数据。 数字病理学形式的生物标本数据，基于机器学习的分类， 定量病理组学特征集。我们将建立一个精心策划的高质量的知识库， 登记研究正在收集其数据的受试者的数字化病理学图像。这些 将对图像进行处理，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群队列， 客观成像和临床属性。数字病理学衍生功能的具体示例 集合包括肿瘤浸润淋巴细胞的定量和分割， 癌症或基质细胞核的表征。功能还将包括光谱和空间 潜在病理学的特征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 （病理学特征）是病理学报告中描述的内容的定量替代。的 重要的区别在于这些特征是定量的和可再现的，不像人类 高度定性的观察结果，并受到观察者间和观察者内的高度影响 可变性该数据集将提供一个独特的、基于全人群组织的癌症视图， 大大加速了我们对疾病发展阶段的理解，癌症 结果，并预测和评估治疗效果。 这项工作将与三个SEER登记册合作进行。我们将合作 在项目开发阶段（UG3），与新泽西州癌症登记处合作。 在项目（UH3）的验证阶段，格鲁吉亚州和肯塔基州州癌症研究所 登记册将加入该项目。基础设施将与以下机构密切合作开发： SEER注册表确保与注册表流程的一致性、可扩展性和能力支持 创建跨多个登记中心的人群队列。我们将部署可视化分析工具 为便于建立流行病学研究的人口群组， 可视化功能集群和相关的全载玻片图像，同时提供先进的 用于进行基于内容的图像检索的算法。科学验证 拟议的环境将通过三项研究，在前列腺癌，淋巴瘤， 和NSCLC，由三个研究中心的研究者领导。

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.