QuBBD: Collaborative Research: Towards Automated Quantitative Prostate Cancer Diagnosis

病理学家评审员对图像的主观分析受到评价者间变异性和吞吐量问题的困扰。然而，随着数字病理学全切片扫描仪变得越来越普遍，研究人员和临床医生可用的高质量病理学图像数据量正在增加，并且新发现的数字形式的病理学图像的广泛可用性，包括NCI癌症基因组图谱（TGCA），开辟了使用计算方法来利用其内在信息进行诊断，预后和精准医学的新可能性。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学的新概念。目前前列腺癌分级的标准是Gleason评分，这是一种基于高级组织结构和腺体形状和组织分析的主观评级系统。然而，Gleason评分在病理学审查者之间是可变的，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，捕获病理图像中前列腺腺体的结构特征。然后，这些描述符可以用于帮助病理学家提供更多的定量和更可重复的类似物，传统的格里森评分，他们可能有独立的预后价值。它们还可以用于对载玻片进行分类，以便与当前的黄金标准组织病理学和分子表征相比，区分不同类型的腺体癌性结构。特别是，这个项目的目的是证明有效性的计算方法的基础上的工具，从计算几何和拓扑结构，以识别和量化腺体的建筑特征。腺体密度将是第一个量化的建筑特点，在这项合作工作。 该奖项由美国国立卫生研究院大数据到知识（BD2K）计划与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.