Computerized Quantitative Imaging Assessment of Tumor Burden

肿瘤负荷的计算机定量成像评估

• 批准号: 7767479
• 负责人: Daniel L Rubin
• 金额: $ 60.96万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7767479
• 关键词: Asses; Biological Markers; Clinical; Clinical Research; Clinical Trials; Colon Carcinoma; Complement; Computer software; Data; Dimensions; Disease; Evaluation; Follicular Lymphoma; Health; Human; Image; Individual; Lesion; Malignant Neoplasms; Measurable; Measures; Medical; Metadata; Methods; Oncologist; Patients; Radiology Specialty; Research Infrastructure; Research Personnel; System; Technology; Time; Treatment Effectiveness; Tumor Burden; Variant; Work; anticancer research; base; cancer Biomedical Informatics Grid; cancer diagnosis; clinical practice; cohort; computerized; effective therapy; image processing; imaging modality; improved; meetings; open source; radiologist; response; software development; tool; treatment response

DESCRIPTION (provided by applicant): Quantitative imaging methods promise to improve the ability of cancer researchers to evaluate tumor burden and treatment response, but progress is thwarted by the lack of software infrastructure to record quantitative imaging information efficiently and reproducibly in the routine clinical workflow, and by the inability to store and share image metadata in standard formats. Many different quantitative imaging features that could more completely describe tumor burden are not being captured because collecting this information is laborious without tool support. Our objective is to develop software infrastructure that meets these needs of cancer researchers through three aims: (1) creating tools leveraging caBIG technologies to standardize quantitative imaging assessment of tumor burden. These tools will enable comprehensive and reproducible assessment of the quantitative imaging features of tumor burden as part of the routine clinical workflow and will improve the coordination of radiologists and oncologists in collecting quantitative image data. Through a commercial partnership, we will incorporate features of our tools in a commercial image interpretation workstation to introduce our methods into clinical practice; (2) developing methods to analyze quantitative image metadata and to help oncologists evaluate quantitative criteria on images collected as part of clinical trials; and (3) evaluating the utility of our infrastructure by applying our tools in two clinical trials and demonstrating the ability of our software infrastructure to quantitatively and more reproducibly measure tumor burden, helping researchers to assess the response to treatment in individual patients and patient cohorts. Our infrastructure will provide new ways of looking at quantitative imaging information related to treatment response along multiple dimensions so that researchers can recognize the effectiveness of treatments in clinical trials better and potentially sooner than using current unassisted approaches. Our work will accelerate quantitative imaging in cancer research, and will provide an essential complement to other centers in the Quantitative Imaging Network that focus on individual quantitative imaging methods. RELEVANCE: The methods and tools we develop will improve the ability of cancer researchers to collect and use quantitative imaging data to accurately assess tumor burden and to develop improved methods for evaluating whether treatment is effective. Improving the accuracy of quantitative imaging in assessing treatment response in individual patients will enable better treatment choices and improve human health.

描述（由申请人提供）：定量成像方法有望提高癌症研究人员评估肿瘤负荷和治疗反应的能力，但由于缺乏在常规临床工作流程中有效和可重复记录定量成像信息的软件基础设施，以及无法以标准格式存储和共享图像元数据，进展受阻。许多不同的定量成像特征，可以更完整地描述肿瘤负荷没有被捕获，因为收集这些信息是费力没有工具的支持。我们的目标是通过三个目标开发满足癌症研究人员这些需求的软件基础设施：（1）创建利用caBIG技术的工具，以标准化肿瘤负荷的定量成像评估。这些工具将使全面和可重复的评估的定量成像特征的肿瘤负荷的常规临床工作流程的一部分，并将提高协调放射科医生和肿瘤学家在收集定量图像数据。通过商业合作，我们将在商业图像判读工作站中整合我们的工具功能，将我们的方法引入临床实践;（2）开发分析定量图像元数据的方法，并帮助肿瘤学家评估作为临床试验一部分收集的图像的定量标准;和（3）通过在两个临床试验中应用我们的工具来评估我们的基础设施的效用，并证明我们的软件基础设施定量和更可重复地测量肿瘤负荷的能力，帮助研究人员评估个体患者和患者队列对治疗的反应。我们的基础设施将提供新的方法来观察与治疗反应相关的定量成像信息，沿着多个维度，以便研究人员可以更好地识别临床试验中治疗的有效性，并且可能比使用当前的无辅助方法更快。我们的工作将加速癌症研究中的定量成像，并将为定量成像网络中专注于个体定量成像方法的其他中心提供必要的补充。 相关性：我们开发的方法和工具将提高癌症研究人员收集和使用定量成像数据的能力，以准确评估肿瘤负荷，并开发改进的方法来评估治疗是否有效。提高定量成像在评估个体患者治疗反应方面的准确性将使更好的治疗选择成为可能，并改善人类健康。