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: 9564836
• 负责人: Jayashree Kalpathy-Cramer
• 金额: $ 67.56万
• 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/9564836
• 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 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整合不同数据类型（例如分子、临床）的信息学方法来对疾病的生物学基础、疾病的治疗和治疗反应做出新的发现。人类成像是表型信息的丰富来源，可以与这些其他数据整合，但生物学家在很大程度上无法将其用于研究，因为其中包含的信息 它们通常不是定量的。向更大的社区提供可视化组织的图像和定量特征，有望加速研究和发现，包括癌症成像生物标志物的开发。 癌症成像生物标志物开发和使用的第一个关键步骤是将目标病变与其环境分开。一旦病变被分割，人们就可以通过计算来表征许多病变图像特征，以便与其他数据类型集成。为了加速开发和优化病变分割和表征算法的进展，我们将开发、部署和传播一个信息学平台。基于云的图像生物标记优化平台 (C-BIBOP) 将包括 1) 本地存储或通过癌症成像档案等精选存储库访问的成像数据，2) 一组可在这些或新上传的数据上运行的分割和特征计算算法，3) 这些数据的病变分割算法的输出，4) 这些数据的特征计算算法的输出，以及 5）一组指标和可视化工具，用于比较这些算法、细分和特征的性能。 具体来说，我们将通过开发基于云的基础设施来支持定制的计算环境、包括图像和相关元数据的“实验”以及对分割和表征结果进行比较、统计分析和可视化的报告模块，来开发用于多机构定量图像数据的大规模集中分析的C-BIBOP。基础设施最初将填充由哥伦比亚大学、麻省总医院、莫菲特和斯坦福大学 (CMMS) 研究人员开发的“基线”算法、分割和图像描述符以及有限的数据集。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”，并使用最先进的算法和精心策划的数据集在参与机构对其进行评估。最后，我们从定量成像网络中确定了一组早期采用者和 beta 测试者，以及表示愿意为 C-BIBOP 贡献算法、数据和结果的外部合作者和工业合作伙伴。我们将托管至少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分割和特征。

项目成果

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

评估模拟病变作为胸部 CT 容量测量临床病变的替代物：国际挑战的结果。

• DOI: 10.1016/j.acra.2018.07.022
• 发表时间: 2019
• 期刊: Academic radiology
• 影响因子: 4.8
• 作者: Robins,Marthony;Kalpathy-Cramer,Jayashree;Obuchowski,NancyA;Buckler,Andrew;Athelogou,Maria;Jarecha,Rudresh;Petrick,Nicholas;Pezeshk,Aria;Sahiner,Berkman;Samei,Ehsan
• 通讯作者: Samei,Ehsan