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

描述（由申请人提供）：越来越多的高质量数字扫描仪的可用性使得能够生成大量组织学图像，共聚焦/多通道图像以及伴随的元数据。然而，缺乏健壮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，开源、记录和进一步开发综合技术，CDSA是我们开发的一种工具，用于促进NCI癌症基因组图谱（TCGA）提供的数据分析。这个工具不是，也不会局限于TCGA数据的分析，但是通过从已经可用的公共数据往回推，我们可以确保开发的信息技术是可扩展的，并且可以被癌症社区使用。在我们的提案中，我们将首先通过软件工程审查过程，以提高安装的便利性，以便分发给其他研究小组。我们已经与Kitware合作完成了这个提案，允许我们使用他们在构建和维护高质量开源软件方面15年以上的经验。提案的其余部分将集中于新功能的测试和集成，例如执行图像量化（例如细胞计数，细胞分析），图像标记和标记的能力，以及执行基本的组级分析，允许将成像特征与用户定义的感兴趣的变量相关联。例如，用户可以根据核的平均密度对单个幻灯片进行分类，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.