Advanced Development of an Open-source Platform for Web-based Integrative Digital Image Analysis in Cancer

描述(申请人提供)：高质量数字扫描仪的日益普及使得能够产生大量的组织学图像、共焦/多通道图像和伴随的元数据。然而，缺乏强大的开源解决方案来有效地可视化、处理和管理这些不断增长的图像集合。我们的目标是利用我们在癌症数字幻灯片档案(CDSA)方面的经验，开放源代码、记录并进一步开发综合技术，CDSA是我们开发的工具，用于促进NCI的癌症基因组图谱(TCGA)提供的数据分析。这个工具不会也不会局限于对TCGA数据的分析，然而，通过从现有的公共数据向后工作，我们可以确保开发的信息学技术是可扩展的，并可供癌症社区使用。在我们的提案中，我们将首先经过软件工程审查的过程，以提高安装的简易性，以便于分发给其他研究小组。我们与Kitware就这一提议进行了合作，允许我们利用他们15年以上在构建和维护高质量开源软件方面的经验。提案的其余部分将侧重于新功能的测试和整合，例如执行图像量化(如细胞计数、细胞轮廓分析)、图像标记和标记以及执行基本组级分析的能力，从而使成像特征与用户定义的感兴趣变量相关联。例如，用户可以基于核的平均密度对单个载玻片进行分类，并将该成像参数与患者生存或肿瘤分级相关联。

DESCRIPTION (provided by applicant): The increasing availability of high-quality digital scanners has enabled the generation of large collections of histology images, confocal/multichannel images, and accompanying metadata. However there is a dearth of robust open- source solutions to efficiently visualize, process and manage these ever growing imaging collections. Our goal is to open-source, document and further develop integrative technologies leveraging our experience with the Cancer Digital Slide Archive (CDSA), a tool we have developed to facilitate analysis of data provided by the NCI's the Cancer Genome Atlas (TCGA). This tool is NOT and will not be limited to the analysis of TCGA data, however by working backwards from public data already available we can ensure the informatics technologies developed are scaleable and usable by the cancer community. In our proposal, we will first go through a process of software engineering review to improve the ease of installation to facilitate distribution to other research groups. We have partnered with Kitware for this proposal allowing us to use their 15+ years of experience in building and maintain quality open source software. The rest of the proposal will focus on the testing and integration of new features such as the ability to perform image quantification (e.g. cell counting, cell profiling), image markup and labeling, as well as perform basic group level analysis allowing the correlation of imaging features with user defined variables of interest. As an example, a user may classify an individual slide based on the mean density of nuclei and correlate this imaging parameter with patient survival, or with tumor grade.