Quantitative Image Informatics for Cancer Research (QIICR)

描述（由申请人提供）：通过软件提取和处理形态计量和功能生物标志物，成像在改善癌症研究方面具有巨大的未开发潜力。在无细胞毒性治疗剂、多模态图像引导消融治疗和快速发展的计算资源的时代，定量成像软件可以在实现微创、客观和可重复的癌症治疗反应评估方面具有变革性。后处理算法是不可或缺的高通量分析和细粒度分化多分子目标。用于此类分析的软件工具必须具有鲁棒性，并在多个主题、时间点和机构收集的一系列数据集中得到验证。确保该软件的有效性需要明确的分析协议规范、分析结果文档以及解释分析结果的明确指导方针。然而，癌症研究数据并没有以促进定量分析进步的格式存在，也缺乏支持公共数据交换和方法共享的基础设施。因此，我们建议开发和传播可互操作的图像信息学平台，用于开发定量成像生物标志物发现的软件工具。该平台将使新的分析工具和定量分析方法的性能评估所产生的数据的存档、组织、检索和传播成为可能。其功能将根据正在进行的QIN研究项目在前列腺癌、头颈癌和多形性胶质母细胞瘤定量成像生物标志物开发方面的需求来确定。该基础设施将基于3D切片器，这是一个由NIH资助的用于图像分析和可视化的开源平台，并将附有样本数据和逐步文档。我们将(1)为特定的癌症成像研究应用开发封装分析和数据组织工作流程的软件工具；(2)支持社会认可的可互操作的开放格式，使分析结果得以传播和分享；(3)开发与社区癌症成像库的接口，使分析结果能够存档和传播。

DESCRIPTION (provided by applicant): Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets. Software tools used for such analyses must be robust and validated across a range of datasets collected for multiple subjects, timepoints and institutions. Ensuring the validity of this software requires unambiguous specification of analysis protocols, documentation of the analysis results, and clear guidelines for their interpretation. Yet cancer research data does not exist in formats that facilitate advancement of quantitative analysis and there is lack of an infrastructure to support common data exchange and method sharing. We therefore propose to develop and disseminate interoperable image informatics platform for development of software tools for quantitative imaging biomarker discovery. This platform will enable archival, organization, retrieval, dissemination of the data produced by the novel analysis tools and performance evaluation of quantitative analysis methods. Its functionality will be defined by the needs of the active QIN research projects in quantitative imaging biomarker development for prostate adenocarcinoma, head and neck cancer and glioblastoma multiforme. The infrastructure will be based on 3D Slicer, an NIH funded open source platform for image analysis and visualization, and will be accompanied by sample data and step-by-step documentation. We will (1) develop software tools encapsulating analysis and data organization workflows for the specific cancer imaging research applications; (2) implement support for interoperable open formats accepted in the community to enable dissemination and sharing of the analysis results; (3) develop interfaces to community cancer imaging repositories to enable archival and dissemination of the analysis results.