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: 9753130
• 负责人: SANDY A. NAPEL
• 金额: $ 56.75万
• 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/9753130
• 关键词: Algorithmic Software; Algorithms; Architecture; Biological; Biological Markers; Cancer Biology; Clinical Data; Clinical Trials; Communities; Computational algorithm; Computer software; Data; Data Set; Development; Digital Imaging and Communications in Medicine; Eastern Cooperative Oncology Group; Evaluation; Failure; Follicular Lymphoma; Funding; Gene Expression; Generations; Genomics; Health; Human; Image; Infrastructure; Investigation; Java; Language; Lesion; Libraries; Link; Location; Machine Learning; Malignant Neoplasms; Measurement; Metabolic; Modality; Modernization; Molecular; Multi-Institutional Clinical Trial; Non-Small-Cell Lung Carcinoma; Outcome; Patient-Focused Outcomes; Pharmaceutical Preparations; Phenotype; Plug-in; Positron-Emission Tomography; Prediction of Response to Therapy; Privatization; Progression-Free Survivals; Pythons; RNA Sequences; Radiogenomics; Research; Research Personnel; Resources; Role; Science; Shapes; Specific qualifier value; System; Therapeutic; Time; Tissues; Tumor Burden; base; cancer biomarkers; cancer genomics; cancer imaging; cancer subtypes; cancer therapy; clinical predictors; cloud based; disorder subtype; feature detection; image archival system; image processing; imaging biomarker; imaging modality; improved; interest; novel; novel therapeutics; oncology; open source; predict clinical outcome; predictive modeling; public health relevance; quantitative imaging; repository; response; specific biomarkers; statistics; success; survival prediction; tool; 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(由申请人提供)：定量成像网络(QIN)是一个由开发定量成像特征的中心组成的联盟，这些特征被证明是潜在癌症生物学的有价值的生物标记物，可用于评估治疗反应和预测临床结果。现在重要的是发现最好的定量成像特征来检测对治疗的反应，识别癌症的亚型，并与癌症基因组学相关联。然而，由于缺乏计算、比较、评估和传播这些量化成像功能所需的共享软件算法、体系结构和资源，进展受到阻碍。我们建议开发定量成像功能管道(QIFP)，这是一个基于云的开源平台，将允许研究人员免费访问这些功能，并加快将定量图像生物标记物引入单中心和多中心临床试验。QIFP将有助于评估新图像特征集与现有图像特征集的增量价值。它 还将允许研究人员添加他们自己的算法，在他们自己的研究中计算新的量化图像特征，并将它们传播到更大的研究社区。为了实现这一点：(1)我们将创建一个可扩展的定量成像特征算法库，能够全面表征癌症的成像表型。它将支持以多种语言实施的广泛的成像模式和算法，包括提供对病变大小、形状、边缘清晰度和像素统计的体积和时间变化评估的算法。(2)我们将构建一个基于云的软件架构，用于创建、执行和比较定量图像特征生成管道，包括库中的算法和/或秦或其他研究人员作为插件提供的算法。QIFP还将具有(A)允许用户指定量化图像特征可用于预测的因变量(例如，无进展存活率)的机器学习引擎，以及(B)比较特定特征用于预测因变量的效用的评估引擎。(3)我们将通过四种方式对QIFP进行评估：(A)通过它概括已知生物标记物在相关临床试验中的作用的能力；(B)通过比较线性测量、新陈代谢肿瘤负担和我们库中用于预测一年无进展生存的特征的新组合；(C)通过将成像特征与已知的宿主、药物和基于肿瘤的滤泡性淋巴瘤生物标记物合并，以开发对患者结果最稳健和综合的预测模型。以及(D)使用QIFP结合和评估由另一个QI团队和我们自己的NCI资助团队在非小细胞肺癌放射基因组学研究中开发的图像特征算法。QIFP将通过提供工具和基础设施来评估癌症新的定量成像特征的价值，从而填补秦国和社区目前正在开展的科学领域的重大空白，从而将加快将新的成像生物标记物纳入单中心和多中心临床试验和肿瘤学实践。