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.