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

• 批准号: 7891013
• 负责人: JOHN M. BUATTI
• 金额: $ 60.72万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 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/7891013
• 关键词: Acetates; Address; Algorithms; Antineoplastic Protocols; Archives; Beds; Bioinformatics; Calibration; Cancer Patient; Cancer Research Network; Clinical; Clinical Data; Clinical Trials; Collaborations; Commit; Communities; Community Clinical Oncology Program; Complex; Computer Systems Development; 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; 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

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强大团队，以及与NIST的合作和与西门子的公私合作伙伴关系。该团队是一个理想的团队，能够实现这一UOI计划公告的目标，并为设想的定量成像网络(QIN)做出积极贡献。我们计划通过以下方式实现我们的目标：具体目标1：利用现有的大型H&N癌症数据集和caBIG，为定量图像分析工具的开发和测试创造一个可访问的环境。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，用于开发、实施和验证通过caBIG和国家癌症成像档案馆(NCIA)向QIN提供的共享工具。1B：我们将连接我们治疗前和治疗后H&N癌症患者的大型PET/CT成像和临床结果数据库，作为一个通用的可访问的测试数据库，将与QIN共享。具体目标2：开发新的半自动工具，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，以便可以比较和最佳地选择图像分析工具进行临床癌症试验。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的适用性和可及性。具体目标3：建立可靠的定量成像质量程序和标准，与NIST和QIN合作，为临床决策提高定量成像的质量和可靠性。3A：使用GE-68 NIST可追踪体模定义和验证PET成像的校准程序、工具和标准。3b：为4D PET/CT 3c定义和验证可靠的质量保证工具和标准：使用包括磁共振成像在内的ACR和GE模体，为定量成像响应评估定义类似的工具和标准。具体目标4：调整和加强基于图像的定量反应评估，为临床试验决策提供支持。3A：在NCI资助的一项针对头颈癌患者的FDG和F-18氟胸苷的现有研究中。3b是在NCI资助的肺癌FDG和C-11醋酸酯成像现有研究中，3c是在计划中的高级别胶质瘤MRSI和F-18氟多巴的临床试验中。