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: 8787268
• 负责人: Jayashree Kalpathy-Cramer
• 金额: $ 74.46万
• 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/8787268
• 关键词: Algorithms; Biological; Biological Markers; Brain; Brain Neoplasms; Breeding; Characteristics; Clinical; Clinical Data; Collection; Communities; Data; Data Set; Descriptor; Development; Disease; Environment; Funding; Grant; Health; Human; Image; Imagery; Informatics; Institution; Investigation; Lesion; Link; Malignant Neoplasms; Metadata; Metric; Molecular; Monitor; Non-Small-Cell Lung Carcinoma; Output; Participant; Performance; Phenotype; Reporting; Research; Research Infrastructure; Research Personnel; Resources; Running; Science; Scientist; Source; Source Code; System; Testing; Time; Tissues; Vendor; anticancer research; base; cancer imaging; cancer type; cloud based; image archival system; image processing; imaging Segmentation; in vivo; lung imaging; open source; public health relevance; repository; research study; response; software development; 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使用信息学方法，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相结合，但由于其中包含的信息，生物学家在很大程度上无法在他们的研究中使用