Quantitative Image Informatics for Cancer Research (QIICR)

描述(由申请人提供)：通过提取和处理形态测量和功能生物标记物的软件，成像在改善癌症研究方面具有巨大的未开发潜力。在非细胞毒性治疗药物、多模式图像引导的消融治疗和快速发展的计算资源的时代，定量成像软件在实现对癌症治疗反应的微创、客观和可重复性评估方面具有变革性。后处理算法对于多分子靶标的高通量分析和细粒度区分是不可或缺的。用于这种分析的软件工具必须是可靠的，并在为多个主题、时间点和机构收集的一系列数据集上进行验证。为了确保该软件的有效性，需要明确说明分析方案、记录分析结果并为其解释提供明确的指导方针。然而，癌症研究数据不存在促进定量分析进步的格式，也缺乏支持共同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记物发现的软件工具。该平台将能够对新型分析工具产生的数据进行存档、组织、检索、传播，并对量化分析方法进行业绩评价。它的功能将由活跃的QIN研究项目在前列腺癌、头颈癌和多形性胶质母细胞瘤的定量成像生物标记物开发方面的需求来定义。该基础设施将基于3D Slicer，这是美国国立卫生研究院资助的用于图像分析和可视化的开源平台，并将伴随着样本数据和逐步文档。我们将(1)开发软件工具，为特定的癌症成像研究应用程序封装分析和数据组织工作流程；(2)支持社区接受的可互操作的开放格式，以实现分析结果的传播和共享；(3)开发与社区癌症显像库的接口，以实现分析结果的存档和传播。

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.