Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers

计算、优化和评估定量癌症成像生物标志物

• 批准号: 8960049
• 负责人: SANDY A. NAPEL
• 金额: $ 65.26万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8960049
• 关键词: Algorithmic Software; Algorithms; Architecture; Biological; Biological Markers; Cancer Biology; Clinical; Clinical Data; Clinical Trials; Communities; Computer software; Computer-Assisted Image Analysis; Data; Data Set; Development; Eastern Cooperative Oncology Group; Evaluation; Failure; Follicular Lymphoma; Funding; Gene Expression; Generations; Genomics; Health; Human; Image; Investigation; Java; Language; Lesion; Libraries; Link; Location; Machine Learning; Malignant Neoplasms; Measurement; Metabolic; Modality; Molecular; Multi-Institutional Clinical Trial; Non-Small-Cell Lung Carcinoma; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Plug-in; Positron-Emission Tomography; Progression-Free Survivals; Pythons; RNA Sequences; Radiogenomics; Research; Research Infrastructure; Research Personnel; Resources; Role; Science; Shapes; Specific qualifier value; System; Therapeutic; Time; Tissue Survival; Tissues; Tumor Burden; base; cancer genomics; cancer imaging; cancer therapy; cloud based; disorder subtype; image archival system; image processing; imaging biomarker; imaging modality; improved; interest; novel; novel therapeutics; oncology; open source; predictive modeling; public health relevance; quantitative imaging; repository; response; statistics; success; tool; transcriptome sequencing; treatment response; tumor; vector; web based interface

 DESCRIPTION (provided by applicant): The Quantitative Imaging Network (QIN) is a consortium of centers developing quantitative image features, which are proving to be valuable biomarkers of the underlying cancer biology and that can be used for assessing response to treatment and predicting clinical outcome. It is now important to discover the best quantitative imaging features for detection of response to therapeutics, to identify subtypes of cancer, and to correlate with cancer genomics. However, progress is thwarted by the lack of shared software algorithms, architectures, and resources required to compute, compare, evaluate, and disseminate these quantitative imaging features within the QIN and the broader community. We propose to develop the Quantitative Imaging Feature Pipeline (QIFP), a cloud-based, open source platform that will give researchers free access to these capabilities and hasten the introduction of quantitative image biomarkers into single- and multi-center clinical trials. The QIFP will facilitate assessment of the incremental value of new vs. existing image feature sets. It will also allow researchers to add their own algorithms to compute novel quantitative image features in their own studies and to disseminate them to the greater research community. To accomplish this: (1) We will create an expandable library of quantitative imaging feature algorithms capable of comprehensive characterization of the imaging phenotype of cancer. It will support a broad set of imaging modalities and algorithms implemented in a variety of languages, including algorithms that provide volumetric and time-varying assessment of lesion size, shape, edge sharpness, and pixel statistics. (2) We will build a cloud-based software architecture for creating, executing, and comparing quantitative image feature-generating pipelines, including algorithms in the library and/or those supplied by QIN or other researchers as plug-ins. QIFP will also have (a) a machine learning engine that lets users specify a dependent variable (e.g., progression-free survival) that the quantitative image features can used to predict, and (b) an evaluation engine that compares the utility of particular features for predicting the dependent variable. (3) We will assess the QIFP in four ways: (a) by its ability to recapitulate the role of known biomarkers in a related clinical trial, (b) by comparing linear measurement, metabolic tumor burden and novel combinations of the features in our library for predicting one-year progression-free survival, (c) by merging imaging features with known host-, drug- and tumor-based follicular lymphoma biomarkers in order to develop the most robust and integrative predictive model for patient outcomes, and (d) by using the QIFP to combine and to evaluate image feature algorithms developed by another QIN team and our own NCI- funded team in the study of radiogenomics of non-small cell lung cancer. The QIFP will fill a substantial gap in the science currently being carried out in the QIN and in the community by providing the tools and infrastructure to assess the value of novel quantitative imaging features of cancer, and will thereby accelerate incorporating new imaging biomarkers into single and multi-center clinical trials and into oncology practice.

 描述（由适用提供）：定量成像网络（QIN）是一个开发定量图像特征的中心联盟，它们提供了基本癌症生物学的有价值的生物标志物，可用于评估对治疗和预测临床结果的反应。现在，重要的是要发现最佳的定量成像特征，以检测对治疗的反应，鉴定癌症的亚型并与癌症基因组学相关。但是，由于缺乏共享软件算法，架构和资源所需的资源来计算，比较，评估和传播QIN和更广泛的社区所需的这些定量成像功能所需的共享软件算法，架构和资源。我们建议开发基于云的开源平台定量成像功能管道（QIFP），该平台将使研究人员自由访问这些功能，并加快将定量图像生物标志物引入单中心和多中心临床试验。 QIFP将有助于评估新的与现有图像特征集的增量值。它 还将允许研究人员添加自己的算法来计算自己的研究中新型的定量图像特征，并将其传播到更大的研究界。为此：（1）我们将创建一个扩展的定量成像特征特征算法库，这些算法能够全面表征癌症的成像表型。它将支持以多种语言实现的广泛的成像方式和算法，包括对病变大小，形状，边缘清晰度和像素统计的算法进行体积和时间变化的评估。 （2）我们将构建一个基于云的软件体系结构，用于创建，执行和比较定量图像生成的管道，包括库中的算法和/或QIN或其他研究人员作为插件提供的算法。 QIFP还将具有（a）机器学习引擎，该引擎可以使用户指定一个因变量（例如，无进展生存），可以使用定量图像特征来预测，以及（b）比较特定功能以预测因变量的评估引擎。 （3）我们将通过四种方式评估QIFP：（a）通过比较线性测量，代谢性肿瘤伯恩和我们图书馆中特征的新型组合来概括已知生物标志物在相关临床试验中的作用，（b）预测一年的无效生存，（c）由Biorge Trumber-Biorge，（c）进行基于Bior的特征，（C）对患者预后的最健壮，最集成的预测模型，以及（d）使用QIFP在非小细胞肺癌的非小细胞肺癌的放射基因组学研究中，由另一个QIN团队和我们自己的NCI资助的团队组合和评估图像特征算法。 QIFP将通过提供工具和基础设施来评估癌症的新型定量成像特征的价值，从而加速将新成像的生物标志物加速为单一和多中心临床试验，并将其加速编码新成像，从而填补QIN和社区中目前正在进行的科学的巨大空白。