TCIA Sustainment and Scalability - Platforms for Quantitative Imaging Informatics in Precision Medicine

TCIA 持续性和可扩展性 - 精准医学中的定量成像信息学平台

• 批准号: 10013134
• 负责人: Imon Banerjee
• 金额: $ 158.49万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-22 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10013134
• 关键词: Advisory Committees; Algorithms; Area; Big Data; Biology; Cancer Biology; Characteristics; Clinical; Clinical Trials; Collaborations; Collection; Communities; Computer software; Coupled; Data; Data Collection; Data Set; Diagnostic Imaging; Disease; Ensure; Fostering; Funding; Goals; Image; Individual; Information Resources; Malignant Neoplasms; Manuals; Medical Research; Morphologic artifacts; National Research Council; Pathology; Patients; Phenotype; Play; Predisposition; Process; Publications; Reproducibility; Research; Resource Informatics; Resources; Semantics; Targeted Research; Techniques; Technology; Testing; The Cancer Imaging Archive; Training; Translational Research; United States National Institutes of Health; Validation; Visual; anticancer research; base; cancer imaging; cancer therapy; cohort; cost; data exploration; data format; data resource; data reuse; data sharing; experience; image archival system; imaging informatics; improved; individual variation; individualized medicine; innovation; interest; knowledge base; meetings; multimodality; new technology; open data; operation; outcome forecast; pathology imaging; precision medicine; precision oncology; quantitative imaging; radiological imaging; radiomics; repository; research study; resource guides; response; tool; treatment planning; validation studies; wiki

Project Summary The National Research Council has defined Precision Medicine as “the tailoring of medical treatments to individual characteristics of each patient.” This requires the ability to classify patients into specialized cohorts that differ in their susceptibility to a particular disease, in the biology and/or prognosis of the diseases they may develop, or in their response to a specific treatment. Identifying quantitative imaging phenotypes across scale through the use of radiomic/pathomic analyses is an evolving approach to cohort identification and to improving our understanding of cancer biology. These analytic techniques require large collections of well-curated data for algorithm testing and validation. Additional big data collections are required to test new hypotheses relating to cancer biology, prognosis and therapy response. Since 2011 the Cancer Imaging Archive (TCIA) has encouraged and supported cancer-related open science research by acquiring, curating, hosting and managing collections of multi-modal information. To remain relevant to its current research community and ready to support future research initiatives TCIA must undergo continuous improvement and expansion of it capabilities guided by the research community. The TCIA user community has identified four critical areas for improvement: expanded resources for integrative Image-Omics studies, enhanced capacity to acquire high quality data collections, resources to support validation studies and Research Reproducibility, and increased community engagement. The sustainment of TCIA and research community directed expansion of its capabilities will ensure this valuable resource continues to support its rapidly growing user community and continue to promote research reproducibility and data reuse in cancer precision medical research.

项目摘要 国家研究委员会将精确医学定义为“医疗治疗的裁缝 每个患者的个人特征。”这需要将患者分为专业人群的能力 它们对特定疾病的敏感性，生物学和/或可能的疾病预后的差异 开发或对特定治疗的反应。识别范围的定量成像表型 通过使用放射线/致病分析是一种不断发展的识别方法，并改善了 我们对癌症生物学的理解。这些分析技术需要大量策划的数据收集 算法测试和验证。需要其他大数据收集来检验与 癌症生物学，预后和治疗反应。自2011年以来，癌症成像档案（TCIA）具有 通过获取，策划，托管和管理与癌症相关的开放科学研究的鼓励和支持 多模式信息的集合。保持与当前的研究社区的关系，并准备支持 未来的研究举措TCIA必须经历IT能力的持续改进和扩展 由研究界。 TCIA用户社区已经确定了改进的四个关键领域： 扩大了整合图像词的研究资源，增强了获取高质量数据的能力 收集，支持验证研究和研究可重复性的资源，并增加了社区 订婚。 TCIA和研究社区的维持指示其能力扩展将确保 这种宝贵的资源继续支持其快速增长的用户社区，并继续促进研究 癌症精确医学研究中的可重复性和数据再利用。