QuBBD: Collaborative Research: Towards Automated Quantitative Prostate Cancer Diagnosis

病理审稿人对图像的主观分析受到评分间变异性和吞吐量问题的困扰。然而，随着数字病理整片扫描仪变得越来越普遍，研究人员和临床医生可以获得的高质量病理图像数据的数量正在增加，并且新发现的数字形式病理图像的广泛可用性，包括NCI癌症基因组图谱（TGCA），为使用计算方法利用其中固有的信息进行诊断、预后和精准医学开辟了新的可能性。该奖项支持启动一项合作研究项目，旨在发现新的基于定量图像的前列腺癌预后生物标志物，重点研究应用于前列腺癌腺体结构的计算拓扑新概念。目前的前列腺癌分级标准是Gleason评分，这是一种基于高层次组织结构和腺体形状和组织分析的主观评分系统。然而，Gleason评分在病理审稿人之间是可变的，并且可能无法捕获腺体生长模式中包含的所有潜在预后信息。在这个项目中，将开发新的拓扑描述符来捕捉病理图像中前列腺的结构特征。这些描述符可以用来帮助病理学家，为传统的格里森评分提供更多的定量和可重复的类似物，并且它们可能具有独立的预后价值。与目前的金标准组织病理学和分子表征相比，它们还可以用于对载玻片进行分类，以区分不同类型的腺体癌结构。特别地，这个项目的目的是证明使用基于计算几何和拓扑工具的计算方法来识别和量化腺状建筑特征的有效性。腺密度将是这个合作项目中第一个量化的建筑特征。该奖项由美国国立卫生研究院大数据到知识（BD2K）计划与美国国家科学基金会数学科学部合作支持。

Subjective analyses of images by pathologist reviewers are plagued by issues of inter-rater variability and throughput. However, as digital pathology whole slide scanners become more commonplace, the amount of high-quality pathology image data available to researchers and clinicians is increasing, and the newfound widespread availability of pathology images in digital form, including the NCI Cancer Genome Atlas (TGCA), opens up new possibilities to use computational approaches to leverage the information inherent within them for diagnosis, prognosis, and precision medicine. This award supports initiation of a collaborative research project that aims to discover new quantitative image-based prognostic biomarkers for prostate cancer, focusing on an investigation of novel concepts from computational topology applied to prostate cancer glandular architecture. The current standard for prostate cancer grading is the Gleason score, which is a subjective rating system based on an analysis of high-level tissue architecture and glandular shape and organization. However, Gleason scoring is variable between pathology reviewers, and may not capture all of the potentially prognostic information contained in glandular growth patterns. In this project, new topological descriptors will be developed that capture architectural features of prostate glands in pathology images. These descriptors can then be used to aid pathologists by providing more quantitative and more reproducible analogs to the traditional Gleason scores, and they may have independent prognostic value. They can also be used to classify slides in order to distinguish between different types of cancerous architectures of glands, compared to the current gold-standard histopathological and molecular characterization. In particular, the aim of this project is to demonstrate effectiveness of using computational methods based on tools from computational geometry and topology to recognize and quantify glandular architectural features. Glandular density will be the first architectural feature quantified in this collaborative work. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.