Quantitative Imaging to Assess Response in Cancer Therapy Trials

描述（由申请人提供）：在爱荷华州大学，我们组建了一支非常强大的团队，包括肿瘤学家、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，并与NIST合作，与西门子建立了公私合作伙伴关系。这个小组是一个理想的团队，以解决这个UOI计划公告的目标，并为设想的定量成像网络（QIN）做出强有力的贡献。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为定量图像分析工具的开发和测试创建一个可访问的环境。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和国家癌症成像档案馆（NCIA）开发，实施和验证QIN共享工具。1b：我们将连接我们的大型PET/CT成像和治疗前后H&N癌症患者的临床结果数据库，作为一个普遍可访问的测试数据库，将与QIN共享。具体目标2：开发用于基于图像的定量反应评估的新型半自动化工具。2a：我们将设计、实施和验证开源二维和三维半自动定量图像分析工具，用于肿瘤定义和适用于缓解评估的定量指标。我们还将开发和应用先进的决策支持软件以及测量可变性工具，以便可以比较图像分析工具并为临床癌症试验选择最佳工具。2b.我们将在成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：建立稳健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可追溯体模定义并验证PET成像的校准程序、工具和标准。3b：定义并验证4D PET/CT 3c的强大质量保证工具和标准：使用ACR和GE体模定义MRI定量成像反应评估的类似工具和标准，包括MRSI。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：在现有的，NCI资助的研究FDG和F-18氟胸苷在头颈癌患者。3b在一项现有的、NCI资助的肺癌FDG和C-11醋酸盐成像研究中，3c：在一项计划中的高级别胶质瘤MRSI和F-18氟多巴临床试验中。

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.