Development of an Open-Source Preclinical Imaging Informatics Platform for Cancer Research

开发用于癌症研究的开源临床前成像信息学平台

• 批准号: 10685380
• 负责人: Daniel Scott Marcus
• 金额: $ 70.79万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685380
• 关键词: Advisory Committees; Animal Experiments; Animal Model; Animals; Architecture; Big Data; Biology; Clinical Research; Clinical Trials; Collaborations; Collection; Complex; Computer software; Data; Data Commons; Data Set; Databases; Detection; Development; Drug Evaluation; Environment; Genetically Engineered Mouse; High Performance Computing; Image; Industry; Informatics; Institution; Internet; Intervention; Laboratories; Malignant Neoplasms; Metadata; Modeling; Mus; Pathology; Patients; Pharmaceutical Preparations; Physiology; Positron-Emission Tomography; Precision Medicine Initiative; Prediction of Response to Therapy; Privatization; Procedures; Process; Protocols documentation; Reproducibility; Research; Research Personnel; Research Support; Resources; Running; Services; Site; Technology; The Cancer Imaging Archive; Time; Tumor Burden; Vertebral column; animal imaging; anticancer research; automated image analysis; automated segmentation; base; cancer imaging; clinical imaging; co-clinical trial; computational pipelines; data management; data mining; data sharing; design; disease diagnosis; ex vivo imaging; experimental study; handheld mobile device; imaging informatics; in vivo; instrument; mathematical analysis; mathematical model; member; multimodality; multiparametric imaging; multiscale data; novel; novel therapeutic intervention; open data; open source; portability; pre-clinical; precision medicine; preclinical imaging; preclinical study; programs; public health relevance; radiomics; response biomarker; serial imaging; software development; treatment response; tumor; tumor xenograft; web interface

ABSTRACT Preclinical imaging is widely used in cancer research to devise novel tumor detection strategies, assess tumor burden and physiology/biology, as well as to validate novel therapeutic strategies and predictive and biomarkers of response to therapy. More recently, the use of patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs) has ushered an era of co-clinical trials where preclinical studies can inform clinical trials, thus potentially bridging the translational gap in cancer research. However, differences in the deployment of the instruments, differences in imaging formats and protocols, differences in animal models, and variability in analytic pipelines among other factors result in non-tractable data and poor reproducibility. Importantly, current databases are not compatible with complexity and growing demands in preclinical cancer imaging which include big data needs and collection of metadata/annotation to support NCI’s precision medicine initiative. Thus, there is an unmet need to develop a unifying imaging informatics and workflow management platform to support cancer research, which will ultimately support the premise of translational precision medicine. We propose to develop an open-source preclinical imaging informatics platform—Preclinical Imaging XNAT- enabled Informatics (PIXI)—to manage the workflow of preclinical imaging laboratories, harmonize imaging databases, and enable deployment of analytic and computational pipelines in preclinical imaging. PIXI will be based on XNAT as the underlying informatics architecture. XNAT is used by over 200 academic institutions and industry entities as the backbone for data management across a wide range of imaging applications in clinical research, and thus offers a robust platform for the development and deployment of PIXI. Through this effort, we will 1) develop the PIXI database and server to capture preclinical imaging associated data, metadata, and preclinical imaging workflow and experiments; 2) develop the PIXI “point-of-service” interface, notebook capabilities, and software development kit (SDK); and 3) develop the PIXI container-based application (“App”) environment to implement portable analytic pipelines. Overall, the development of a preclinical imaging informatics platform is expected to have a profound impact on the management of preclinical imaging in cancer research which will ultimately support translational precision medicine.

摘要 临床前成像被广泛应用于癌症研究，以设计新的肿瘤检测策略，评估肿瘤 负担和生理学/生物学，以及验证新的治疗策略和预测性和生物标志物 对治疗的反应。最近，患者来源的肿瘤异种移植(PDX)和基因移植的使用 工程小鼠模型(GEMM)开启了联合临床试验的时代，临床前研究可以为 临床试验，从而潜在地弥合了癌症研究中的翻译差距。然而，两国之间的差异 仪器的部署，成像格式和协议的差异，动物模型的差异，以及 除其他因素外，分析管道中的可变性导致数据难以处理，重复性较差。 重要的是，目前的数据库与临床前癌症的复杂性和不断增长的需求不兼容 成像，包括大数据需求和元数据/注释的收集，以支持NCI的精准医疗 主动权。因此，开发统一的成像信息学和工作流管理的需求尚未得到满足 支持癌症研究的平台，最终将支持翻译精准医学的前提。 我们建议开发一个开源的临床前影像信息学平台-临床前影像XNAT- 启用信息学(Pixi)-管理临床前成像实验室的工作流程，协调成像 数据库，并允许在临床前成像中部署分析和计算管道。皮西将会是 基于XNAT作为底层信息学架构。XNAT被200多个学术机构使用， 作为临床影像应用程序中数据管理的中坚力量的行业实体 研究，从而为皮西的开发和部署提供了一个强大的平台。通过这一努力，我们 将1)开发Pixi数据库和服务器以捕获临床前影像相关数据、元数据和 临床前影像工作流程和实验；2)开发Pixi“服务点”界面，笔记本 能力和软件开发工具包(SDK)；3)开发基于Pixi容器的应用程序(App) 实现可移植分析管道的环境。总体而言，临床前成像的发展 信息学平台有望对癌症临床前影像管理产生深远影响 最终将支持翻译精准医学的研究。