Informatics Tools for Optimized Imaging Biomarkers for Cancer Research&Discovery

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，以发现有关疾病生物学基础、疾病治疗和治疗反应的新发现。人类成像是可以与这些其他数据整合的表型信息的丰富来源，但是生物学家在很大程度上无法在他们的研究中使用它们，因为其中包含的信息 它们通常不是定量的。将可视化组织的图像和定量表征提供给更大的社区，对于加速研究和发现，包括癌症成像生物标志物的开发，具有很大的希望。 在癌症中开发和使用成像生物标志物的第一个关键步骤是从其环境中分割靶病变。一旦病变被分割，就可以计算地表征许多病变图像特征以与其他数据类型集成。为了加快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志物优化平台（C-BIBOP）将包括1）本地存储或通过策划的存储库（如癌症成像档案）访问的成像数据，2）一组分割和特征计算算法，可在这些或新上传的数据上运行，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）用于比较这些算法、分割和特征的性能的一组度量和可视化工具。 具体来说，我们将开发C-BIBOP，用于多机构定量图像数据的大规模集中分析，方法是开发基于云的基础设施，以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。基本的基础设施最初将由哥伦比亚、MGH、Moffitt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填充。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，以及外部合作者和工业合作伙伴，他们表示愿意为C-BIBOP贡献算法，数据和结果。我们将举办至少两个永久的在线图像集，并保持最好的分割和表征，可供参与者随时使用。

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.