Quantitative Imaging to Assess Response in Cancer Therapy Trials

 描述(由申请人提供)：爱荷华大学(UI)在其第一个赠款周期中为量化成像网络(QIN)的目标和使命做出了重大贡献。UI Qin网站的整体创新是通过成功实施的云状分布式工作流基础设施来加速交付工具的生成。我们才华横溢、经验丰富的跨学科团队包括肿瘤学家、核医学内科医生、放射科医生、物理学家、电气和计算机工程师、生物信息学家和统计学家。我们提出了以下新的具体目标，这些目标以高度创新的方式创造性地建立在以前工作的基础上，并有助于在本资助期加快QIN的进展：具体目标1：利用我们成功的I期开发和验证的高度自动化的定量图像分析方法，开发一个新颖、强大的基于成像基因组学的决策支持平台，应用于链接和公开可用的精心策划的图像(TCIA)和分子(TCGA)数据仓库，以及已建立的H&N癌症结果数据库。这将促进未来风险适应性试验所需的新方法，这些试验肯定会包括基因组和定量图像数据。具体目标2：在第一阶段开发和验证的图像分析工具的基础上建立和创新：a)将高度和全自动化的定量图像分析方法应用于H&N癌症的合作小组数据集，b)通过创造性的新图像分析方法开发独特的新工具，用于H&N癌症的Flt/PET，骨盆和骨髓的Flt/PET，以及用于神经内分泌癌的肝转移的DOTATOC PET/CT。这些新的方法将公之于众，并将有助于未来的临床试验、决策支持、定量成像反应评估和各种癌症部位的治疗靶向。具体目标3：利用我们的图像分析和决策支持工具，在我们已建立的PET定量和校准模体之间建立新的联系，以创建临床实用的开源自动模体分析工具，该工具可应用于旨在提高临床试验定量成像质量保证的国家努力 多种方式，包括正电子发射计算机断层扫描、CT和核磁共振成像。这将提供一个关键的工具来改进 临床试验数据采集的简便性、准确性和协调性。具体目标4：通过相关的积极临床试验，在临床试验决策支持中采用、增强和推广基于图像的定量反应评估。重点介绍了几项临床试验：1)Flt/PET作为骨盆恶性肿瘤化疗放射治疗中骨髓活性和毒性的预测指标，2)DOTATOC作为神经内分泌肿瘤疾病负担和治疗反应的指标，以及3)定量磁共振成像[T2，T1，T1]，定量易感性图谱(QSM)和磁共振成像(MRSI)作为静脉注射大剂量维生素C治疗恶性胶质瘤反应的有效预测因子。这些试验将促进定量图像分析工具的发展。 未来临床试验中的开发、决策支持工具和风险适应方法。

 DESCRIPTION (provided by applicant): The University of Iowa (UI) has contributed significantly to the quantitative imaging network (QIN) goals and mission during its first grant cycle. The overall innovation of the UI QIN site is accelerated deliverable tool generation accomplished through a successfully implemented cloud-like distributed workflow infrastructure. Our diversely talented and experienced interdisciplinary team includes oncologists, nuclear medicine physicians, radiologists, physicists, electrical and computer engineers, bioinformaticists, and statisticians. We propose the following new specific aims that build creatively from previous work in a highly innovative fashion and help accelerate QIN progress during this funding period: Specific Aim 1: Develop a novel, robust imaging genomics-based decision support platform using a combination of our successful Phase-I developed and validated highly automated quantitative image analysis methods applied to linked and publically-available well curated image (TCIA) and molecular (TCGA) data warehouses along with an established outcomes database for H&N cancers. This will facilitate new methods necessary for future risk adaptive trials that will certainly include both genomic and quantitative image data. Specific Aim 2: Build and innovate based on Phase-I developed and validated image analysis tools: a) Apply highly and fully automated quantitative image analysis methods to a cooperative group data set of H&N cancers, b) Develop unique new tools through creative new image analysis methods for application to FLT/PET in H&N cancer, FLT/PET in pelvis and bone marrow, as well as DOTATOC PET/CT for liver metastases in neuroendocrine cancers. These novel approaches will be made publicly available and will contribute to future clinical trials, decision support, quantitative imaging response assessment and therapy targeting in a variety of cancer sites. Specific Aim 3: Create a novel link between our established work in PET quantification and calibration phantoms with our image analysis and decision support tools to create a clinically practical open source automated phantom analysis tool that can be applied to national efforts aimed to improve quantitative imaging quality assurance for clinical trials across multiple modalities including PET, CT, and MRI. This will provide a critical tool for improving the ease, accuracy and harmonization for clinical trials data acquisition. Specific Aim 4: Adapt, enhance and extend quantitative image-based response assessment in clinical trial decision-support through relevant active clinical trials. Several clinical trials are highlighted exploring:1) FLT/PET as a predictor of bone marrow activity and toxicity in pelvic malignancies treated with chemoradiotherapy, 2) DOTATOC as an indicator of disease burden and treatment response in neuroendocrine tumors and 3) quantitative MR imaging [T2, T1, T1¿, quantitative susceptibility mapping (QSM) and MRSI] as effective predictors of response in malignant glial tumors treated with intravenous high dose vitamin C. These trials will facilitate quantitative image analysis tool development, decision support tools and risk adaptive approaches in future clinical trials.