QuBBD: Collaborative Research: Towards Automated Quantitative Prostate Cancer Diagnosis

QuBBD：合作研究：实现前列腺癌自动化定量诊断

基本信息

批准号：
1557716
负责人：
Brittany Fasy
金额：
$ 4.66万
依托单位：
Montana State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-15 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557716&HistoricalAwards=false
关键词：
QuBBD Collaborative Research Towards Automated

项目摘要

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.

病理学家审稿人对图像的主观分析受到评估者间的变异性和吞吐量的困扰。但是，随着数字病理的整个幻灯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图像数据的数量正在增加，并且新发现的以数字形式的病理图像广泛可用性，包括NCI癌症基因组地图（TGCA），包括使用新的可能性来使用计算方法来掌握其内在的诊断和诊断的诊断。该奖项支持了一个协作研究项目的启动，该项目旨在发现针对前列腺癌的新的基于图像的预后生物标志物，重点是对用于前列腺癌症腺体结构的计算拓扑的新概念的研究。当前的前列腺癌分级标准是格里森评分，这是一个主观评分系统，基于对高级组织结构以及腺体形状和组织的分析。但是，格里森评分在病理学审查员之间是可变的，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，以捕获病理图像中前列腺的建筑特征。然后，这些描述符可用于通过为传统的格里森分数提供更定量和更可重复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。与当前的金标准的组织病理学和分子表征相比，它们还可以用于分类幻灯片，以区分不同类型的腺体癌结构。特别是，该项目的目的是证明使用基于计算几何学和拓扑的工具来识别和量化腺体体系结构特征的工具的有效性。腺体密度将是该协作工作中量化的第一个架构特征。该奖项得到了美国国立卫生研究院大数据的支持（BD2K）倡议，并与国家科学基金会数学科学部合作。