Quantitative Imaging to Assess Response in Cancer Therapy Trials

定量成像评估癌症治疗试验中的反应

基本信息

批准号：
8034225
负责人：
JOHN M. BUATTI
金额：
$ 56万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8034225
关键词：
Acetates Address Algorithms Antineoplastic Protocols Beds Bioinformatics Calibration Cancer Patient Cancer Research Network Clinical Clinical Data Clinical Trials Collaborations Commit Communities Community Clinical Oncology Program Complex Computer software Computers Data Data Set Databases Decision Making Development Dopa Drug Kinetics Early treatment Engineering Environment Evaluation Faculty Financial compensation Four-dimensional Funding Future Glioma Goals Head and Neck Cancer Healthcare Human Image Image Analysis Imaging Device Informatics Institutes Iowa Lead Lung Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Malignant Neoplasms Malignant neoplasm of lung Manuals Measurement Measures Metadata Methods Metric Modeling NIH Program Announcements Oncologist Outcome PET/CT scan Participant Phase Phase I Clinical Trials Phase II Clinical Trials Play Positron-Emission Tomography Principal Investigator Procedures Reproducibility Research Research Infrastructure Research Project Grants Scientist Secure Software Tools Specialist Systems Development Technology Testing Tracer Universities Validation Work base bioimaging cancer Biomedical Informatics Grid cancer imaging cancer therapy clinical decision-making clinically relevant design driving force experience head and neck cancer patient image archival system imaging modality improved indexing middleware molecular imaging novel oncology open source programs public-private partnership quality assurance relational database response system architecture tool tool development translational study tumor uptake working group

项目摘要

DESCRIPTION (provided by applicant): At the University of Iowa we have assembled a remarkably strong team of oncologists, clinical imaging specialists, computer scientists, imaging and therapy physicists, bioinformatics specialists, and statisticians, as well as collaboration with NIST and a public/private partnership with Siemens. This group is an ideal team to address the goals of this UOI Program Announcement and contribute robustly to the envisioned Quantitative Imaging Network (QIN). We plan to achieve our goals through: Specific Aim 1: Create an accessible environment for quantitative image analysis tool development and testing using an existing large H&N cancer dataset and caBIG. 1a: We plan to design and deploy a broadly connected informatics system architecture to support a locally networked relational database for development, implementation and validation of shared tools to the QIN through caBIG and the National Cancer Imaging Archive (NCIA). 1b: We will Interface our large PET/CT imaging and clinical outcomes database of pre and post therapy H&N cancer patients as a universally accessible test database that will be shared with the QIN. Specific Aim 2: Develop novel semi-automated tools for quantitative image-based response assessment. 2a: We will design, implement, and validate open source 2D and 3D semi- automated quantitative image analysis tools for tumor definition and quantitative metrics applicable to response assessment. We will also develop and apply advanced decision-support software as well as measurement variability tools so that image analysis tools can be compared and optimally selected for clinical cancer trials. 2b. We will test these image analysis tools on the imaging and clinical data to assess applicability and accessibility by QIN. Specific Aim 3: Establish robust quality procedures and standards for quantitative imaging, working with NIST and the QIN to enhance the quality and reliability of quantitative imaging for clinical decision-making. 3a: Define and validate calibration procedures, tools and standards for PET imaging using a Ge-68 NIST-traceable phantom. 3b: Define and validate robust quality assurance tools and standards for 4D PET/CT 3c: Define similar tools and standards for quantitative imaging response assessment using ACR and GE phantoms for MRI, including MRSI. Specific Aim 4: Adapt and Enhance Quantitative Image-based Response Assessment for Clinical Trials Decision-Support. 3a: In an existing, NCI-funded study of FDG and F-18 fiuorothymidine in head and neck cancer patients. 3b In an existing, NCI-funded study of FDG and C-11 acetate imaging in lung cancer and 3c: In a planned clinical trial of MRSI and F-18 fluorodopa in high-grade gliomas.

描述（由申请人提供）：在爱荷华大学，我们组建了一支由肿瘤学家、临床成像专家、计算机科学家、成像和治疗物理学家、生物信息学专家和统计学家组成的非常强大的团队，并与 NIST 和公众/与西门子的私人合作伙伴关系。该小组是实现 UOI 计划公告目标并为设想的定量成像网络 (QIN) 做出强有力贡献的理想团队。我们计划通过以下方式实现我们的目标：具体目标 1：使用现有的大型 H&N 癌症数据集和 caBIG 创建一个可访问的环境，用于定量图像分析工具的开发和测试。 1a：我们计划设计和部署一个广泛连接的信息学系统架构，以支持本地联网的关系数据库，通过 caBIG 和国家癌症影像档案馆 (NCIA) 开发、实施和验证 QIN 共享工具。 1b：我们将把治疗前和治疗后 H&N 癌症患者的大型 PET/CT 成像和临床结果数据库连接起来，作为一个普遍可访问的测试数据库，并与 QIN 共享。具体目标 2：开发新颖的半自动化工具，用于基于图像的定量反应评估。 2a：我们将设计、实施和验证开源 2D 和 3D 半自动定量图像分析工具，用于肿瘤定义和适用于反应评估的定量指标。我们还将开发和应用先进的决策支持软件以及测量变异性工具，以便可以对图像分析工具进行比较并针对临床癌症试验进行最佳选择。 2b.我们将在影像和临床数据上测试这些图像分析工具，以评估 QIN 的适用性和可访问性。具体目标 3：建立健全的定量成像质量程序和标准，与 NIST 和 QIN 合作，提高临床决策定量成像的质量和可靠性。 3a：使用 Ge-68 NIST 可追踪模型定义并验证 PET 成像的校准程序、工具和标准。 3b：定义和验证 4D PET/CT 的强大质量保证工具和标准 3c：使用 ACR 和 GE 体模进行 MRI（包括 MRSI）的定量成像反应评估定义类似的工具和标准。具体目标 4：调整和增强基于图像的定量反应评估以支持临床试验决策。 3a：NCI 资助的一项针对头颈癌患者的 FDG 和 F-18 氟胸苷的现有研究。 3b 在一项由 NCI 资助的现有肺癌 FDG 和 C-11 醋酸盐成像研究中，以及 3c：计划中的 MRSI 和 F-18 氟多巴在高级别神经胶质瘤中的临床试验。