Computerized Quantitative Imaging Assessment of Tumor Burden

肿瘤负荷的计算机定量成像评估

• 批准号: 8657856
• 负责人: Daniel L Rubin
• 金额: $ 54.43万
• 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 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8657856
• 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担和治疗反应的能力，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，进展受到阻碍。许多可以更完整地描述肿瘤负荷的不同定量成像特征没有被捕获，因为在没有工具支持的情况下收集这些信息是很费力的。我们的目标是通过以下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，以标准化肿瘤负担的定量成像评估。作为常规临床工作流程的一部分，这些工具将能够对肿瘤负荷的定量成像特征进行全面和可重复的评估，并将改善放射科医生和肿瘤科医生在收集定量图像数据方面的协调。通过商业合作，我们将把我们的工具的特点纳入商业图像解释工作站，将我们的方法引入临床实践；(2)开发定量图像元数据分析方法，帮助肿瘤学家评估临床试验中收集的图像的定量标准；(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，并展示我们的软件基础设施定量和更可重复地测量肿瘤负担的能力，帮助研究人员评估个体患者和患者队列的治疗反应。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，从而使研究人员能够更好地认识到临床试验中治疗的有效性，而且可能比使用目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。

项目成果

Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.

• DOI: 10.1109/jbhi.2015.2478255
• 发表时间: 2016-11
• 期刊: IEEE journal of biomedical and health informatics
• 影响因子: 7.7
• 作者: Diamant I;Hoogi A;Beaulieu CF;Safdari M;Klang E;Amitai M;Greenspan H;Rubin DL
• 通讯作者: Rubin DL

Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.

使用 AIM 图像注释和本体对癌症肿瘤进行自动分期。

• DOI: 10.1007/s10278-019-00251-x
• 发表时间: 2020
• 期刊: Journal of digital imaging
• 影响因子: 4.4
• 作者: Luque,EF;Miranda,N;Rubin,DL;Moreira,DA
• 通讯作者: Moreira,DA

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.

• DOI: 10.1016/j.media.2014.06.009
• 发表时间: 2014-10
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Kurtz, Camille;Depeursinge, Adrien;Napel, Sandy;Beaulieu, Christopher F.;Rubin, Daniel L.
• 通讯作者: Rubin, Daniel L.

Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound.

通过超声进行自动活检前甲状腺癌风险评估。

• 发表时间: 2017
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Galimzianova,Alfiia;Siebert,SeanM;Kamaya,Aya;Desser,TerryS;Rubin,DanielL
• 通讯作者: Rubin,DanielL

Intelligent Word Embeddings of Free-Text Radiology Reports.

自由文本放射学报告的智能词嵌入。

• 发表时间: 2017
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Banerjee,Imon;Madhavan,Sriraman;Goldman,RogerEric;Rubin,DanielL
• 通讯作者: Rubin,DanielL