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: 8964178
• 负责人: JOHN M. BUATTI
• 金额: $ 61.75万
• 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 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8964178
• 关键词: American College of Radiology Imaging Network; Ascorbic Acid; Bioinformatics; Bone Marrow; Calibration; Classification; Clinical Trials; Clinical Trials Design; Collaborations; Collection; Computers; Data; Data Set; Databases; Decision Making; Development; Discipline of Nuclear Medicine; Dose; Eastern Cooperative Oncology Group; Effectiveness; Engineering; Evolution; Funding; Future; Generations; Genomics; Goals; Grant; Head and Neck Cancer; Head and neck structure; Image; Image Analysis; Informatics; Intravenous; Iowa; Leadership; Letters; Link; Magnetic Resonance Imaging; Malignant Glioma; Malignant Neoplasms; Maps; Metastatic Neoplasm to the Liver; Methods; Mission; Modality; Molecular; Neuroendocrine Tumors; Neurosecretory Systems; Oncologist; Outcome; PET/CT scan; Pelvis; Performance; Phase; Physicians; Positron-Emission Tomography; Predisposition; Research Infrastructure; Research Personnel; Risk; Site; Talents; Teleconferences; Testing; The Cancer Genome Atlas; Therapy trial; Tissues; Toxic effect; Universities; Work; anticancer research; base; burden of illness; cancer care; cancer imaging; cancer site; cancer therapy; chemoradiation; data acquisition; data sharing; experience; genetic information; image archival system; imaging informatics; improved; innovation; magnetic resonance spectroscopic imaging; meetings; novel; novel strategies; oncology; open source; public health relevance; quality assurance; quantitative imaging; radiologist; reconstruction; response; targeted treatment; tool; tool development; treatment response; tumor; working group

 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.

 描述（由适用提供）：爱荷华大学（UI）在第一个赠款周期中为定量成像网络（QIN）目标和任务做出了重大贡献。 UI QIN站点的总体创新是通过成功实现的类似云的分布式工作流基础架构来加速可交付的工具生成的。我们有才华和经验丰富的跨学科团队包括肿瘤学家，核医生，放射科医生，医生，电气和计算机工程师，生物信息学家和统计学家。 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 publicly-available well curated image (TCIA) and molecular (TCGA) data warehouses along具有H＆N癌症的既定成果数据库。这将促进未来风险自适应试验所需的新方法，这些方法肯定包括基因组和定量图像数据。具体目标2：基于I期开发和经过验证的图像分析工具的构建和创新工具：a）将高度和完全自动化的定量图像分析方法应用于H＆N癌症的教练组数据集，b）通过在H＆n癌症中应用FLT/PET的FLT/PET，在Pelvis和Bone Marrow and cor corn and Dotation and dotatine flt/pet中，通过应用新的图像分析方法来开发独特的新工具，并将其应用于FLT/PET癌症。这些新颖的方法将公开可用，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靶向在各种癌症部位的靶向疗法。具体目标3：通过我们的图像分析和决策支持工具在我们既定的宠物量化和校准幻象之间建立新的联系 多种方式，包括宠物，CT和MRI。这将为改进 临床试验数据获取的轻松，准确性和协调。特定目的4：通过相关的主动临床试验，在临床试验决策支持中适应，增强和扩展基于定量图像的反应评估。探索了一些临床试验：1）FLT/PET是通过化学放疗治疗治疗的骨盆骨髓活性和毒性的预测指标，2）DOTATOC作为疾病伯恩的指标和神经内分泌肿瘤中的治疗反应的指标和3）t2，T2，T1，T1，T1，T1，MRSM MRISTID（Q）用静脉内高剂量维生素C处理的恶性神经胶质肿瘤的反应。这些试验将促进定量图像分析工具 在将来的临床试验中开发，决策支持工具和风险自适应方法。