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之间的可变性和吞吐量问题的困扰。然而，随着数字病理全片扫描仪变得越来越常见，研究人员和临床医生可以获得的高质量病理图像数据量正在增加，新发现的广泛可用的数字形式的病理图像，包括NCI癌症基因组图谱(TGCA)，打开了新的可能性，使用计算方法来利用其中固有的信息来进行诊断、预后和精确医学。该奖项支持启动一个合作研究项目，旨在发现前列腺癌新的基于图像的预后生物标记物，重点是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格里森评分，这是一个基于对高水平组织结构和腺体形状和组织的分析的主观评分系统。然而，Gleason评分在不同的病理评价者之间是可变的，并且可能不能捕捉到腺体生长模式中包含的所有潜在的预后信息。在这个项目中，将开发新的拓扑描述符，以捕捉病理图像中前列腺体的结构特征。然后，这些描述符可以通过提供与传统格里森评分更定量和更具重复性的类似物来帮助病理学家，并且它们可能具有独立的预后价值。与目前金标准的组织病理学和分子表征相比，它们还可以用于对切片进行分类，以便区分不同类型的腺体癌变结构。特别是，这个项目的目的是证明使用基于计算几何和拓扑学工具的计算方法来识别和量化腺体结构特征的有效性。腺体密度将是这项合作工作中量化的第一个建筑特征。该奖项由国家卫生研究院大数据向知识转化(BD2K)倡议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.