Informatics Tools for Optimized Imaging Biomarkers for Cancer Research&Discovery

用于优化癌症研究成像生物标志物的信息学工具

• 批准号: 9334737
• 负责人: Jayashree Kalpathy-Cramer
• 金额: $ 26.22万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9334737
• 关键词: Algorithms; Biological; Brain; Brain Neoplasms; Characteristics; Clinical; Clinical Data; Collection; Communities; Computational algorithm; Custom; Data; Data Set; Descriptor; Development; Disease; Environment; Funding; Grant; Health; Human; Image; Imagery; Industrialization; Informatics; Institution; Investigation; Lesion; Link; Lung CAT Scan; Malignant Neoplasms; Mathematics; Metadata; Modernization; Molecular; Monitor; Non-Small-Cell Lung Carcinoma; Output; Participant; Performance; Phenotype; Precision therapeutics; Reporting; Reproducibility; Research; Research Infrastructure; Research Personnel; Resources; Running; Science; Scientist; Source; Source Code; Statistical Data Interpretation; System; Testing; Time; Tissues; Vendor; Visualization software; anticancer research; cancer biomarkers; cancer imaging; cancer type; cloud based; cloud platform; experimental study; human imaging; image archival system; image processing; imaging Segmentation; imaging biomarker; in vivo; open source; phenotypic data; public health relevance; quantitative imaging; repository; response; software development; specific biomarkers; symposium; tool; tool development; willingness

DESCRIPTION (provided by applicant): Biologists and other human-health related scientists have been employing informatics approaches that integrate disparate data types (e.g. molecular, clinical) to make new discoveries about the biological basis of diseases, the treatment of diseases, and response to therapy. Human imaging is a rich source of phenotypic information that could be integrated with these other data, but they have been largely inaccessible to biologists for use in their investigations because the information contained within them is usually not quantitative. Making images and quantitative characterizations of visualized tissues available to the larger community holds great promise to accelerate research and discovery including the development of imaging biomarkers in cancer. The first critical step in the development and use of imaging biomarkers in cancer is the segmentation of the target lesions from their environments. Once the lesions have been segmented, one can computationally characterize many lesion image features for integration with other data types. To accelerate progress towards developing and optimizing algorithms for lesion segmentation and characterization, we will develop, deploy, and disseminate an informatics platform. The Cloud-based Image Biomarker Optimization Platform (C-BIBOP) will include 1) imaging data stored locally or accessed through curated repositories such as the Cancer Imaging Archive, 2) a set of segmentation and feature computation algorithms that can be run on these or newly uploaded data, 3) the outputs of lesion segmentation algorithms for these data, 4) the outputs of feature computation algorithms for these data, and 5) a set of metrics and visualization tools for the comparison of the performance of these algorithms, segmentations and features. Specifically, we will develop the C-BIBOP for the large-scale central analysis of multi-institutional quantitative image data by developing a cloud-based infrastructure to support customized computing environments, "experiments" that include images and associated meta-data, and a reporting module that performs comparisons, statistical analyses and visualizations of the results of segmentation and characterization. The basic infrastructure will be initially be populated with "baseline" algorithms, segmentations and image descriptors developed by Columbia, MGH, Moffitt, and Stanford (CMMS) investigators as well as limited datasets. We will deploy the C-BIBOP on a cloud platform, develop and share "experiments" consisting of data, algorithms and exploration of parameter spaces, and evaluate it at the participating institutions with state-of-the-art algorithms and well-curated datasets. Finally, we have identified a set of early adopters and beta-testers from within the Quantitative Imaging Network, and external collaborators and industrial partners who have indicated their willingness to contribute algorithms, data and results to C- BIBOP. We will host at least two permanent online collections of images and maintain the best segmentations and characterizations available that can be utilized by participants at anytime.

描述（由申请人提供）：生物学家和其他与人类健康相关的科学家一直在采用信息学方法，以整合不同的数据类型（例如分子，临床），以对疾病的生物学基础，疾病治疗和对治疗的反应进行新发现。人类成像是可以与这些其他数据集成的表型信息的丰富来源，但是生物学家在研究中都无法访问它们，因为其中包含的信息包含在其中 它们通常不是定量的。对较大社区的可视化组织进行图像和定量表征具有巨大的希望，可以加速研究和发现，包括开发癌症成像生物标志物。 癌症成像生物标志物开发和使用的第一步是对靶病变的环境分割。一旦细分病变，就可以在计算上表征许多病变图像特征，以与其他数据类型集成。为了加速发展和优化病变细分和表征算法的进步，我们将开发，部署和传播信息学平台。基于云的图像生物标志物优化平台（C-Bibop）将包括1）通过精选的存储库来存储或通过诸如癌症成像档案的临时存储库中访问的成像数据，2）一组分割和特征计算算法，可以在这些或新上传的数据上运行这些数据，3）这些数据的输出和这些数据的输出，这些数据及其输出库，4），4）计算数据，4） 5）一组指标和可视化工具，用于比较这些算法，分割和功能的性能。 具体来说，我们将开发C-Bibop，用于通过开发基于云的基础架构来支持定制的计算环境，“实验”，其中包括图像和相关的元数据数据，以及对进行比较，统计分析和可视化的分裂和表征的结果。最初，基本基础架构将由哥伦比亚，MGH，Moffitt和Stanford（CMMS）研究人员以及有限的数据集开发的“基线”算法，分割和图像描述符。我们将在云平台上部署C-Bibop，开发和共享由数据，算法和参数空间探索组成的“实验”，并在参与的机构中评估使用最先进的算法和经过良好策划的数据集进行评估。最后，我们已经从定量成像网络中确定了一组早期采用者和β测试器，以及表明他们愿意为C- Bibop贡献算法，数据和结果的外部合作者和工业合作伙伴。我们将至少托管两个永久的在线图像集合，并维护参与者可以随时使用的最佳细分和特征。